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Table of contents :
Contents
A Survey of Machine Learning for Network Fault Management
1 Introduction
2 Network Fault Management
3 Pattern Mining-Based Approaches
3.1 Episode and Association Rules Mining-Based Approaches
3.2 Sequential Pattern Mining-Based Approaches
3.3 Clustering-Based Approaches
3.4 Summary and Perspective
4 Machine Learning-Based Approaches
4.1 Artificial Neural Networks-Based Approaches
4.2 Decision Tree-Based Approaches
4.3 Bayesian Networks-Based Approaches
4.4 Support-Vector Machine-Based Approaches
4.5 Dependency Graph-Based Approaches
4.6 Other Approaches
4.7 Summary and Perspective
5 Conclusion
References
Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes
1 Introduction
2 Deep BGRU Method for Botnet Detection in IoT Networks
2.1 Bidirectional Gated Recurrent Unit
2.2 The Proposed Method for Selection of Optimal BGRU Hyperparameters
2.3 Deep BGRU Classifier for IoT Botnet Detection
3 Results and Discussion
3.1 Influence of Activation Functions on Classification Performance
3.2 Influence of the Number of Epochs on Classification Performance
3.3 Influence of the Number of Hidden Layers on Classification Performance
3.4 Influence of Hidden Units on Classification Performance
3.5 Influence of Batch Size on Classification Performance
3.6 Influence of Optimizers on Classification Performance
3.7 Performance of Deep BGRU-Based Multi-class Classifier
4 Conclusion
References
Big Data Clustering Techniques: Recent Advances and Survey
1 Introduction
2 Clustering Techniques
2.1 Partitioning Methods
2.2 Hierarchical Clustering Methods
2.3 Density-Based Methods
2.4 Grid-Based Algorithms
2.5 Model-Based Methods
3 Big Data Clustering Approaches
3.1 Data Reduction-Based Methods
3.2 Centre-Based Reduction Methods
3.3 Parallel Techniques
4 Big Data Clustering Applications
4.1 Healthcare
4.2 Internet of Things (IoT)
4.3 Anomaly Detection
4.4 Social Media
5 Discussion
6 Challenges and Future Research Work
7 Conclusion
References
A Survey of Network Intrusion Detection Using Machine Learning Techniques
1 Introduction
2 Machine Learning
2.1 Supervised Learning
2.2 Un-Supervised Learning
2.3 Semi-supervised Learning
2.4 Reinforcement Learning
2.5 Ensemble Learning
2.6 Feature Selection
3 Machine Learning Based Intrusion Detection System
3.1 Intrusion Detection System (IDS)
4 Hybrid Intrusion Detection Systems
5 Evaluations of Intrusion Detection System
5.1 KDD Cup-‘99 Dataset
5.2 NSL-KDD Dataset
5.3 Kyoto 2006 + Dataset
5.4 Performance Metrics
6 Research Opportunities
7 Conclusion
References
Indexing in Big Data Mining and Analytics
1 Introduction
1.1 Objective of the Chapter
1.2 Taxonomy of the Chapter
2 Index and Indexing
2.1 Index Architecture and Indexing Types
2.2 Bitmap Index
2.3 Dense Index
2.4 Sparse Index
3 Online Indexes
3.1 Online Indexing
3.2 Database Cracking
3.3 Adaptive Merge
3.4 Big Data Analytics Platforms
4 Inherent Indexes in MapReduce
4.1 Per Document Indexing
4.2 Per-Posting List Indexing
5 User-Defined Indexing in MapReduce
6 Conclusion
References
Two-Steps Wrapper-Based Feature Selection in Classification: A Comparison Between Continuous and Binary Variants of Cuckoo Optimisation Algorithm
1 Introduction
2 Background
2.1 Cuckoo Optimisation Algorithm
2.2 Binary Cuckoo Optimisation Algorithm
2.3 Related Works
3 Proposed Wrapper-Based Feature Selection Approaches
3.1 BCOA and COA for Feature Selection
3.2 A Combined Fitness Function for BCOA and COA Feature Selection
4 Experimental Design
4.1 Experimental Datasets
4.2 Experimental Parameter Settings
4.3 Benchmark Approaches
5 Results and Discussions
5.1 Results of the Proposed BCOA-FS and COA-FS
5.2 Results of the Proposed BCOA-2S and COA-2S
5.3 Comparison Between Proposed Methods and Classical Methods
5.4 Comparison Between Proposed Methods and Other Existing Methods
5.5 Comparisons Between BCOA and COA
5.6 Further Discussions
6 Conclusions and Future Work
References
Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
1 Introduction
2 Related Works
3 Methodology
3.1 Method of Data Collection and Preparation
3.2 Feature Subset Selection
3.3 Meta-Heuristics Algorithms
3.4 Classification
3.5 Cross-Validation
3.6 Evaluation Metric
4 Results and Discussion
4.1 Modelling and Interpretation
4.2 Models Comparison Based on SVM as a Classifier
4.3 Models Comparison Based on RF as a Classifier
4.4 Comparison with Existing Models
5 Conclusion
References
Improved Cloud-Based N-Primes Model for Symmetric-Based Fully Homomorphic Encryption Using Residue Number System
1 Introduction
2 Related Works
3 Research Method
3.1 Homomorphic Encryption
3.2 N-Primes Model
3.3 Residue Number System
3.4 Proposed RNS-Based N-Primes Model for Symmetric FHE
4 Results and Discussion
5 Conclusion
Appendix A: Ciphertext
References
Big Data Analytics: Partitioned B+-Tree-Based Indexing in MapReduce
1 Introduction
1.1 Objective of the Chapter
2 Literature Review
2.1 Inverted Index in MapReduce
2.2 User-Defined Indexing in MapReduce
3 Methodology
3.1 Partitioned B+-Tree
3.2 InputSplit as Component of Choice in the HDFS
4 Experimental Results and Discussion
4.1 The Dataset
4.2 Index Building Using the Datasets
4.3 Test Queries
4.4 The Experiment and Its Setup
4.5 Index Creation Performance Evaluation
5 Results and Discussions
6 Conclusion
References
Internet of Vehicle for Two-Vehicle Look-Ahead Convoy System Using State Feedback Control
1 Introduction
2 System Architecture
2.1 IoV Components
2.2 IoV Architecture for Two-Vehicle Look-Ahead Convoy
2.3 Platform Used for Implementation of the Model
3 Modeling of the Two Look-Ahead Vehicle Convoy Strategy
4 Vehicle Dynamic
4.1 SFC Design Using Pole-Placement Approach
4.2 Design Procedure of the SFC via Pole Placement Technique
4.3 Controllability System Test
5 Result and Discussion
6 Conclusion and Future Work
References
Vehicle Following Control with Improved Information Flow Using Two-Vehicle-Look-Ahead-Plus-Rear-Vehicle Topology
1 Introduction
2 Single-Vehicle External Dynamics
2.1 Aerodynamic Drag
2.2 Viscous Friction Drag
2.3 Rolling Resistance Force
2.4 Simplified Vehicle Dynamics
3 Following Vehicle Convoy Dynamics
4 Turning of Gains and Simulation
5 Results and Discussion
5.1 Comparison of the One-Vehicle Look-Ahead and Two-Vehicle Look-Ahead Against the Proposed Topology
6 Conclusion and Further Work
References
Extended Risk-Based Context-Aware Model for Dynamic Access Control in Bring Your Own Device Strategy
1 Introduction
2 Background
2.1 Dynamic Access Control in BYOD Strategy
2.2 Bayesian Network
3 Related Work
3.1 Risk Evaluation Model
3.2 Context-Aware Access Control Model
3.3 Finding from Related Work
4 Proposed ExtSRAM Model
4.1 Contextual Risk Factors
4.2 Enterprise Environment
5 ExtSRAM Process Flow
5.1 Assumptions on ExtSRAM Model
5.2 ExtSRAM Methodology
6 Theoretical Validation of the Model
6.1 Soundness of ExtSRAM
6.2 Completeness of ExtSRAM
7 Future Research Directions
8 Conclusion
References
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Haruna Chiroma Shafi’i M. Abdulhamid Philippe Fournier-Viger Nuno M. Garcia   Editors

Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics Theories and Applications

Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics

Haruna Chiroma · Shafi’i M. Abdulhamid · Philippe Fournier-Viger · Nuno M. Garcia Editors

Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics Theories and Applications

Editors Haruna Chiroma Mathematical Sciences Abubakar Tafawa Balewa University Bauchi, Nigeria

Shafi’i M. Abdulhamid Information Technology & Cyber Security Community College Qatar Doha, Qatar

Philippe Fournier-Viger School of Computer Science Harbin Institute of Technology Shenzhen, China

Nuno M. Garcia Instituto de Telecomunicações University of Beira Interior Covilha, Portugal

ISBN 978-3-030-66287-5 ISBN 978-3-030-66288-2 (eBook) https://doi.org/10.1007/978-3-030-66288-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

A Survey of Machine Learning for Network Fault Management . . . . . . . . Mourad Nouioua, Philippe Fournier-Viger, Ganghuan He, Farid Nouioua, and Zhou Min Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Segun I. Popoola, Ruth Ande, Kassim B. Fatai, and Bamidele Adebisi Big Data Clustering Techniques: Recent Advances and Survey . . . . . . . . . Ibrahim Hayatu Hassan, Mohammed Abdullahi, and Barroon Isma’eel Ahmad A Survey of Network Intrusion Detection Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas Rincy N and Roopam Gupta

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29 57

81

Indexing in Big Data Mining and Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Ali Usman Abdullahi, Rohiza Ahmad, and Nordin M. Zakaria Two-Steps Wrapper-Based Feature Selection in Classification: A Comparison Between Continuous and Binary Variants of Cuckoo Optimisation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Ali Muhammad Usman, Umi Kalsom Yusof, and Syibrah Naim Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier . . . . . . . . . . . . . . . . 177 Kayode S. Adewole, Muiz O. Raheem, Oluwakemi C. Abikoye, Adeleke R. Ajiboye, Tinuke O. Oladele, Muhammed K. Jimoh, and Dayo R. Aremu Improved Cloud-Based N-Primes Model for Symmetric-Based Fully Homomorphic Encryption Using Residue Number System . . . . . . . 197 K. J. Muhammed, R. M. Isiaka, A. W. Asaju-Gbolagade, K. S. Adewole, and K. A. Gbolagade

v

vi

Contents

Big Data Analytics: Partitioned B+-Tree-Based Indexing in MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Ali Usman Abdullahi, Rohiza Ahmad, and Nordin M. Zakaria Internet of Vehicle for Two-Vehicle Look-Ahead Convoy System Using State Feedback Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Mu’azu Jibrin Musa, Olakunle Elijah, Shahdan Sudin, Suleiman Garba, Saliu Aliu Sala, and Abubakar Abisetu Oremeyi Vehicle Following Control with Improved Information Flow Using Two-Vehicle-Look-Ahead-Plus-Rear-Vehicle Topology . . . . . . . . . . . . . . . . 261 Mu’azu Jibrin Musa, Shahdan Sudin, Zaharuddin Mohamed, Abubakar Abisetu Oremeyi, Yahaya Otuoze Salihu, and Gambo Danasabe Extended Risk-Based Context-Aware Model for Dynamic Access Control in Bring Your Own Device Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Shefiu Olusegun Ganiyu and Rasheed Gbenga Jimoh

A Survey of Machine Learning for Network Fault Management Mourad Nouioua, Philippe Fournier-Viger, Ganghuan He, Farid Nouioua, and Zhou Min

Abstract Telecommunication networks play a major role in today’s society as they support the transmission of information between businesses, governments, and individuals. Hence, ensuring excellent service quality and avoiding service disruptions are important. For this purpose, fault management is critical. It consists of detecting, isolating, and fixing network problems, a task that is complex for large networks, and typically requires considerable resources. As a result, an emerging research area is to develop machine learning and data mining-based techniques to improve various aspects of the fault management process. This chapter provides a survey of data mining and machine learning-based techniques for fault management, including a description of their characteristics, similarities, differences, and shortcomings.

1 Introduction Computer networks are crucial to today’s society as they support communication between individuals, governments, and businesses. They are used not only to connect M. Nouioua · P. Fournier-Viger (B) · G. He Harbin Institute of Technology (Shenzhen), Shenzhen, China e-mail: [email protected] M. Nouioua e-mail: [email protected] G. He e-mail: [email protected] M. Nouioua · F. Nouioua University of Bordj Bou Arreridj, El Anceur, Algeria e-mail: [email protected] F. Nouioua Aix-Marseille Université, LIS, UMR-CNRS 7020, Marseille, France Z. Min Huawei Noah’s Ark Lab, Shenzhen, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_1

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desktop computers, but also all kinds of electronic devices such as smartphones, wearable devices, sensors, and industrial machines. They also play a key role in emerging domains such as sensor networks [66, 87], vehicular networks [86], cloud computing [8], big data analysis [79], and the Internet of Things [81]. To ensure effective and efficient communication between devices, a network must be carefully designed in terms of physical and logical topology, and software must be properly configured. This requires considering various aspects such as budget, facilities, performance, and security requirements. Then, during a network’s lifetime, various maintenance tasks must be carried out such as to replace, install, and upgrade equipment and software. Moreover, a key activity is fault management, which is carried out to ensure a network’s security, availability, reliability, and optimize its performance [21, 71]. Fault management aims at solving problems that are occurring in a network. It consists of four main tasks, which are (1) Detecting, (2) Diagnosing, (3) Isolating, and (4) Fixing network faults [84, 87]. Fault management is not an easy task because faults may be caused by complex interactions between network devices and sometimes only appear for a short time. A good fault management process may consist of (1) Preventive measures and logging solutions that may raise alarms indicating potential problems, (2) A method for prioritizing the most important alarms or faults to analyze, and (3) Appropriate methods to isolate and fix the issues. Fault management can be quite time-consuming and costly, especially for large and heterogeneous networks. Hence, it has become critical to develop improved fault management techniques [84, 87]. Since more then two decades, some attempts at developing computer systems for fault management were made. For instance, in the 1990s, some expert systems were designed that relied on a knowledge base of rules to diagnose network problems. But a drawback of such systems was that specifying rules by hand requires expert knowledge, these rules would not be noise tolerant, and that writing these rules is time consuming and prone to errors [42, 60]. To build computer systems for fault management that do not rely heavily on domain experts, a promising fault management approach has been to apply data mining and machine learning-based techniques [5, 17, 19, 48, 56, 69, 74, 77]. These techniques allow to semi-automatically extract knowledge and learn models from data. Though there has been several studies in this direction, no survey has been published on this topic. This chapter fills this gap by reviewing key studies on data mining and machine learning for fault management. The chapter provides a brief description of each study, their key ideas, and the advantages and limitations of the proposed techniques. The reviewed studies are categorized into two main categories based on the type of algorithms that they used: (1) Pattern mining-based approaches (e.g., itemset mining, association rule mining, and clustering) and (2) Machine learning-based approaches (e.g., neural networks, decision trees, Bayesian networks, and dependency graphs). The rest of this chapter is organized as follows: Sect. 2 reviews some important concepts related to telecommunication network fault management. Then, Sects. 3

A Survey of Machine Learning for Network Fault Management

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and 4 survey data mining-based approaches and machine learning-based approaches, respectively. Finally, a conclusion is drawn.

2 Network Fault Management This section introduces important concepts related to telecommunication networks and fault management. A computer network connects a set of devices that can exchange information and share resources. Typical devices found on a network are end-user computers (e.g., workstations), servers, mobile devices, and networking devices (e.g., routers and switches). Because networks can be used in a wide range of contexts and to address different use cases, numerous hardware and software technologies have been proposed for networking. Choosing a set of technologies requires considering various criteria such as cost, security, and performance. For example, some key requirements for building a sensor network may be to preserve battery life and perform distributed calculations. This is quite different from the requirements for large-scale computer networks such as the Internet, or those of a mobile GSM or UMTS network. The goal of fault management is to detect, identify, and correct malfunctions in telecommunication networks [21, 71, 84, 87]. A fault is a malfunction that occurs on a network and may cause errors. A fault can have various consequences such as making a network device unavailable or degrading its performance. For example, a router hardware malfunction may cause the device to reboot and to be temporarily unavailable. Though a fault may cause several undesirable events, a fault is said to not be caused by any other events. In other words, a fault is the root cause of some error(s). An error is defined as a discrepancy between some observed values and some expected values or the violation of some conditions [71]. An error is caused by a fault and can propagate inside a network causing other errors. Two main types of faults may occur [84, 87]: hard faults (a device cannot communicate with others) and soft faults (a device continues to operate but with an abnormal behavior such as sending corrupted data or incorrectly routing data). Moreover, from the perspective of time, faults can be categorized as permanent (an action must be taken to fix the issue), temporary/transient (the fault may appear only for a short time) and intermittent (the fault may periodically re-appear if no action is taken) [87]. Fault are sometimes not observable for various reasons such that no evidences have been collected. To more easily detect faults, alarms may be raised by network devices. An alarm is a symptom that can be observed of a potential fault. Alarms can be generated by devices or network management systems to provide information about potential faults that may cause errors. Alarms are very important as they allow to infer the existence of faults so that remediation steps can be taken [71]. Many alarms may appear in a network because a single fault may cause multiple alarms and because some alarms may be triggered in situations where no fault has occurred. To facilitate alarm management, alarms are often categorized into different severity levels such as cleared, low, medium, high, and critical. Based on severity levels,

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M. Nouioua et al. 1) Fault detection

2) Fault localization

3) Fault isolation

4) Fault recovery

Fig. 1 A generic network fault management process

alarms may be treated differently [74]. For instance, while an alarm of the cleared level may be ignored, an alarm considered as critical may trigger an emergency message sent by SMS to a network administrator so that he can take quick action. Moreover, alarms can be recorded in logs on each device or can be collected from all devices by some fault management software to facilitate alarm analysis. In passive fault management, each device is responsible of communicating its alarms to other devices or the user, while in active fault management, a device may be periodically probed by other devices to verify its state. Because device clocks may not be synchronized, it is sometimes not possible to know which alarms occurred first. A solution to this issue can be to use distributed algorithms to synchronize logical clocks [50]. A human can investigate an alarm and fix a fault. Besides, it is also possible to put recovery procedures into place (scripts) to automatically handle faults or errors detected by some specific alarm types [71]. Finding the reason(s) why some error occurs in a network can sometimes be quite difficult because of the complex and dynamic interaction between devices, and because some faults are not permanent, errors can propagate or are influenced by other faults or events. For example, some faults may only appear in some circumstances such as when the battery level of a sensor is low. Managing faults in a telecommunication network is generally done by the following steps [71]: (1) Automatically collecting data about alarms generated by devices, (2) Preparing the data and enriching the data with additional information (if needed), (3) Identifying alarms that should be investigated with higher priority and inferring the root cause of alarms, (4) Applying recovery procedures or dispatching technicians to specific locations (physically or virtually) to isolate and fix the issues. This process is illustrated in Fig. 1. Step 1 is called fault detection, Step 2 is named fault localization, while Step 3 is sometimes called fault localization, fault isolation or root cause analysis. Finally, Step 4 is named fault recovery [71]. Another reason why fault management is challenging is that while thousands of alarms may be generated in network devices, the number of technicians or budget for maintaining a network is limited. Hence, it is easy for technicians to be overloaded with thousands of alarms and being unable to investigate all of them. Accordingly, it is critical to be able to prioritize some alarms and their relationships to investigate the most important alarms first. However, identifying the most important alarms is not easy. In some case, some fault in a device may cause many other alarms. Moreover, some alarms may only be triggered in some very specific and complex situations involving multiple devices. The following Sects. 3 and 4 give a survey of the different approaches for fault management using pattern mining and machine learning techniques, respectively.

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3 Pattern Mining-Based Approaches To develop innovative fault management approaches, several studies have used pattern mining techniques. Pattern mining is a key task in the field of data mining, which consists of analyzing data to identify interesting patterns that may help to understand the data or support decision-making [3, 28, 57, 59, 64, 80]. Several pattern mining algorithms have been designed to search for patterns. To apply an algorithm, a user typically has to set constraints on patterns to be found. For example, some algorithms are designed to find frequent patterns (patterns that appear at least a minimum number of times in a database) [3, 28, 59, 64], while others for clustering can find groups of similar data records [80]. Most of pattern mining algorithms are unsupervised as the goal is to discover new knowledge. Pattern mining techniques have been used to analyze telecommunication data and find various types of patterns for fault management. The next three subsections review studies that have used three main types of pattern mining techniques: (1) Episode and association rule mining, (2) Sequential pattern mining and (3) Clustering algorithms.

3.1 Episode and Association Rules Mining-Based Approaches Hatonen et al. [38] and Klemettinen et al. [48] first proposed using pattern mining techniques to analyze telecommunication network data. They designed a system called Telecommunication Alarm Sequence Analyser (TASA) to discover correlations between alarms. Let A be a set of alarm types and T be a set of timestamps. The input of TASA is a sequence of alarms S = (a1 , t1 ), (a2 , t2 ), . . . (an , tn ) that has been recorded from a network, where each alarm ai ∈ A(1 ≤ i ≤ n) is annotated with a timestamp ti ∈ T . From such sequence of alarms, TASA applies a frequent episode mining algorithm [33, 34, 59] to extract episode rules [48]. An episode E is a tuple that has the form a1 , a2 , . . . ak  where ai ∈ A, for all i ∈ {1, . . . , k}. An episode rule has the form E 1 , E 2 , . . . E m ⇒(x,y) a, where E j ⊆ A(1 ≤ j ≤ m) and a ∈ A, and x and y are two amounts of time where y > x. This rule is interpreted as if all alarms of E 1 appears in any order, and then are followed by alarms of E 2 in any order, . . ., and then are followed by alarms of E m in any order, and all of this happens in no more than x seconds, then the alarm a will appear no more than z = (y − x) seconds later. To find interesting episode rules, two measures are used called the support and the confidence. Let there be an episode rule α = (X ⇒(x,y) Y ) and a sequence S, where X and Y are the rule antecedent and consequent, respectively. The support of the episode rule α in TASA is the absolute number of occurrences of the episode X, Y  in the sequence. Formally, given an episode rule α = X ⇒(x,y) Y and a sequence S, Support(α) = |Occ(X, Y , S)|, where Occ(X, Y , S) is the set of time intervals where the episode X, Y  occurs in sequence S.

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Fig. 2 Example of discovering episode rules between alarms with TASA

The confidence measure can be interpreted as the conditional probability that the whole episode occurs within time y given that the left side of the episode rule has already occurred within time x. Formally, we refer to the set of occurrences of an episode X, Y  in a sequence S within time t as Occ(X, Y , S, t). Based on this, the ,S,y)| . confidence of an episode rule α = X ⇒(x,y) Y is Confidence(α) = |Occ(X,Y |Occ(X ,S,x)| The TASA system outputs all rules having at least a minimum support and a minimum confidence [48] defined by the user. Moreover, the user can set constraints on the maximum amount of time for the duration x and y of a rule. Figure 2 shows an example of episode rule extraction from a sequence of alarms using TASA. For this example, only timestamps from the 9th to the 20th s of that sequence are considered. Alarms are denoted as A, B, C, D, E, and F. We can see from Fig. 2 that Occ(A, B, C, S) = {[9, 18], [15, 18]}. Thus, Support(A, B ⇒(x,y) C = 2). If we consider the time durations x and y as x = 3 and y = 5 s, then Occ(A, B, C, S, 5) = {[15, 18]} and Occ(A, B, S, 3) = {[9, 11], [15, 16]}. Accordingly, Confidence(A, B ⇒(3,5) C) = 1/2. Taking another episode rule (C, E ⇒(3,5) F), Since Occ(C, E, F, S) = {[10, 13], [10, 20], [18, 20]}, Support(C, E ⇒(x,y) F) = 3. Moreover, since Occ(C, E, F, S, 5) = {[10, 13], [18, 20]} and Occ(C, E, S, 3) = {[10, 12], [18, 19]}, Confidence(C, E ⇒(3,5) F) = 2/2 = 1. Besides finding episode rules, TASA can also discover interesting associations between alarm properties by applying an association rule mining algorithm [3, 63]. Let there be a set of property values P to describe alarms. Furthermore, let D E = {D1 , D2 , . . . Dl } be the descriptions of l alarm occurrences, where Dh ∈ P(1 ≤ h ≤ l). An association rule has the form X ⇒ Y , where X, Y ⊆ P and X ∩ Y = ∅, and is interpreted as if an alarm occurrence has property values X , then it also has property values Y . For example, a rule link Alar m ⇒ B F may indicate that if an alarm of type link Alar m occurs, it is associated to a network device called B F. Two measures are used to select rules. The support of an association rule X ⇒ Y is calculated as sup(X ⇒ Y ) = |{D|X ∪ Y ⊆ D ∈ D E}|, and the confidence of a rule X ⇒ Y is calculated as con f (X ⇒ Y ) = sup(X ⇒ Y )/|{D|X ⊆ D ∈ D E}|. An example of association rule extraction using TASA is shown in Fig. 3, where properties are denoted as A, B, C, and D. Contrarily to episode rules, association rules do not consider time.

A Survey of Machine Learning for Network Fault Management

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Description of four alarm occurrences Description

Properties

D1

A B C D

D2

A B

D3

C

D4

ABCD

Association rule mining

Association rules A C D A

support = 2 confidence = 100% support = 2 confidence = 66 % support = 2 confidence = 100 % support = 2 confidence = 66 %



Fig. 3 Example of discovering association rules between alarm properties with TASA

The TASA system also offers various tools to visualize rules, sort rules, and group rules having similar properties. A drawback of the TASA system is that it is not suitable for processing long alarm sequences.

3.2 Sequential Pattern Mining-Based Approaches Another pattern mining technique that has been applied for fault management is sequential pattern mining [28, 64]. Lozonavu et al. [56] proposed a method to discover alarm correlation patterns using sequential pattern mining [64]. This method not only discovers sequential patterns indicating interesting relationships between alarms instances, but also studies correlation and the relationships between different network elements such as network nodes and network problems [56]. The input is a sequence of alarms with their timestamps (a1 , t1 ), (a2 , t2 ), . . . (an , tn ), as defined in the previous subsection. The approach of Lozonavu et al. first partitions the input sequence into several sequences based on the timestamps of alarms. Two consecutive alarms (av , tv ) and (av+1 , tv+1 ) of the input sequence are put in the same sequence partition if tv+1 − tv < max Gap, where max Gap is a user-defined parameter. The result is a sequence database containing multiple input sequence partitions. In that sequence database, the timestamps of events are discarded. Then, the PrefixSpan [64] algorithm is applied on that database to find subsequences that appear in at least minsup sequence partitions, where minsup is a parameter set by the user. The result is a set of sequential patterns (frequent subsequences of alarms). A sequential pattern has the form b1 , b2 , b3 , . . . , b y  indicating that some alarm b1 appeared before another alarm b2 , was followed by b3 and so on. Then, the approach of Lozonavu et al. constructs a relationship graph, which shows the relationships between different alarms. This is done by transforming the discovered sequential patterns into relations. More precisely, the relationship graph is a directed weighted graph where each node represents a distinct alarm, a directed edge between two alarms indicates that an alarm occurred before the other in at least one discovered sequential pattern, and the weight of a relation reflects the strength

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Input: a sequence of alarms with timestamps (not shown) maxgap time

Partitioning

Sequence database < A, B, C>

A B C

A C D

A B C D Sequential patterns with support (frequency)

Relationship graph for alarms

A

B

D

C

Graph construction

support = 2 < A, C> support = 3 support = 2 support = 2 support = 2 support = 2 …

Sequential pattern mining with minsup =2

Fig. 4 Example of applying Lozonavu et al.’s approach to study temporal relationships between alarms

of this relation which is calculated by the confidence measure. The confidence of a pattern b1 , b2  is defined as how many sequence partitions contains the pattern b1 , b2  divided by how many sequence partitions contain b1 . An example of using the approach of Lozonavu et al. is illustrated in Fig. 4, where alarms are denoted as A, B, C and D. Lozonavu et al. also presented a second type of relationship graph where the relationships between network devices are studied instead of the alarms. This graph is also derived from the sequential patterns because each alarm instance is associated with a device. It has been demonstrated that these graphs can then be used by network experts to better understand the network behavior and to discover hidden relations between network elements that were not known by network experts. In another study, Wang et al. [74] have presented a system called Automatic Alarm Behavior Discovery (AABD) to study the behavior of alarms and select important alarms that should be brought to the attention of network operators from the thousands of alarms that may occur in a network. The AABD system takes as input a sequence of alarms, where each alarm is described using several fields such as the alarm type, the time that the alarm was produced and that it was cleared, the name of the device that has sent the alarm, and the network domain of the device. AABD first preprocesses the input sequence to filter out invalid alarm instances. An alarm is said to be invalid if some of its fields contain invalid or missing values. For example, an alarm having no timestamp will be discarded as it will not be useful for the subsequent analysis performed by AABD. During the pre-processing step, alarm instances are also grouped by network domains as the behavior of an alarm may not be the same for devices of different domains. Then, AABD applies an algorithm called Transient Flapping Determination (FTD) to detect alarms that are transient, i.e., alarms that usually only appear for a short time before they are cleared. A simple approach to determine if an alarm is transient is

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to calculate the duration of each of its instances. Then, given two parameters Ω and C T , an alarm is called transient (flapping) if the proportion of its instances that have a duration no greater than C T is greater than Ω. Then, for each transient alarm, all alarm instances having a duration that is less than C T are discarded to reduce the number of instances. It was shown that this approach based on transient alarm detection can reduce the number of alarms presented to operators by more than 84% [74]. However, a drawback of this definition is that setting the C T parameter for different alarm types in different situations requires expert knowledge and is time-consuming. Thus, Wang et al. also proposed a method to automatically set these parameters for each alarm type based on the distribution of each alarm’s duration [74]. The settings of the C T parameter for an alarm type is called a flapping rule. Then, AABD divides the sequence of alarms into several sequences to obtain a sequence database and applies the PrefixSpan [64] algorithm to extract frequent sequential patterns representing temporal correlations between alarm instances. This process is similar to the approach of Lozonavu et al. but the transformation from the input sequence to a sequence database is done differently. First, AABD calculates the support (occurrence frequency) of each alarm to find the N most frequent alarms, called main alarms, where N must be set by the user. Then, AABD creates a sequence for each main alarm. The created sequence contains that main alarm and all the alarms that occurred in the same time window width (w) with that main alarm. In other words, the created sequence will contain that main alarm and all the alarms that occurred no more than w/2 s before and w/2 s after. In the experiments, the value of the time window width w was set to 5 min because it was empirically observed that 90% of related alarms occurred within 5 min. Sequential patterns are then extracted from the resulting sequence database. Note that each network domain is treated individually because the behavior of the same alarm type may not be the same for different domains. Then, the AABD system creates rules called P-C Rules (Parent-Child rules) to reduce the number of alarms presented to users. A PC-rule indicates that an alarm (called parent) may cause several other alarms (called child). A PC rule generation is done using an algorithm named PCRG, which takes as input the sequential patterns and also a knowledge-base called the P 2 lookup table. This latter is created by network administrators for each network domain based on historical trouble tickets as well as based on their rich experience. The P 2 table specifies two possible relationships between alarm pairs, that is an alarm may cause another alarm or two alarms are mutually exclusive. In the P 2 lookup table, each potential parent alarm is represented by its name and a serial number which is an ordered list of integers that reveal the ranking property of this potential parent alarm. The PCRG algorithm utilizes the serial numbers to determine the relationship between alarms, to test the possibility of generating a PC rule from each discovered sequential pattern. It was found that PC rules can greatly reduce the number of alarms presented to users. Costa et al. [19] proposed a complete alarm management system. The system’s architecture has two main modules. The first one is a rule management system, which discovers rules in alarm data using a modified sequential pattern mining algorithm (GSP) [70], which reveals alarm correlation and can be used to perform root cause

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analysis. Rules can be edited by hand. Moreover, a reinforcement learning algorithm is applied to evaluate how these rules are used for network malfunction resolution to refine the rule database. The second module performs alarm prioritization using a neural network to select alarms that should be treated with higher priority. The system first pre-processes raw alarm data to keep only the relevant alarm attributes and sorts alarm instances by their start time. Moreover, if several alarm instances of the same type appear within a small amount of type, they are replaced by a single alarm with a counter so that patterns found only contain different alarms. The result is a sequence of alarms with timestamps. Then, sequential pattern extraction is performed by applying a modified GSP algorithm that extracts frequent sequential patterns using a sliding-window. From these patterns, rules are generated, which are evaluated using the lift and confidence measures to filter out spurious rules. To identify root cause problems, each extracted sequence is divided into two parts: The first part is the first alarm instance of the sequence and the second part is the following alarm instances with their occurrence counts. This method has some similarity to the approach of Lozonavu et al. [56] but it can be seen as more elaborated. The approach of Raúl is quite flexible as the sliding-window can be automatically enlarged in specific situations. The system was applied to data from a large Portugese telecommunication company and reduced the number of alarms presented to the user by up to 70%.

3.3 Clustering-Based Approaches Clustering algorithms are another type of unsupervised data mining techniques that have been used to perform alarm correlation and root cause analysis in telecommunication networks. Generally, the goal of clustering is to extract clusters (groups of instances) from a dataset, such that similar instances are grouped together while dissimilar instances are put in different clusters [40]. Instances are described using attributes and similarity between instances can be measured using various measures. Clustering techniques, as other data mining techniques, have been studied for detecting and diagnosing faults in alarms data of telecommunication networks. In the context of alarm correlation analysis, clustering is used to automatically group alarms that occurred within a same short period of time and may have been triggered by the same cause. Clustering is interesting for fault management because it does not require training data and it can group alarms that are related into clusters based on various criteria such as their occurrence times. Then, experts can further analyze these clusters to discover the root cause of problems. For example, it has been suggested that the earliest alarm of a cluster may be considered as its root cause. Sozuer et al. [69] proposed a method to discover clusters in alarm event data. Let A be a set of alarm types. The proposed approach first transforms raw alarm event data into a sequence database, defined as a set of n sequences S D B = {s1 , s2 , . . . , sn }. Each sequence si (1 ≤ i ≤ n) is an ordered list of alarm sets (A1 , t1 ), (A2 , t2 ), . . . (Am , tm ) where each alarm set A j (1 ≤ j ≤ m) has a timestamp t j and A j ⊆ A. Each alarm instance has a start time and clearance time, which defines a time interval called its life cycle. An alarm set is created by picking an

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alarm instance and adding all other alarm instances that have a start time within its life cycle. Moreover, alarm instances within an alarm set are sorted by time. This approach allows capturing that an alarm may trigger other alarms until it is cleared. After the sequence database creation, two weight metrics are calculated for each item (alarm type) in the sequence database. These metrics are the term frequency and inverse item frequency, which are typically used for document classification. Then, item vectors are formed based on these weight vectors. Thereafter, the K means clustering algorithm is applied on the normalized item vectors to obtain a set of clusters where each cluster contains alarms that are highly related in view of the selected weight measure. An experimental evaluation with two weeks of data from a radio network was performed to evaluate the approach. It was found that clusters could be used to perform prediction more accurately than using the RuleGrowth [32] sequential rule mining algorithm. A limitation of the approach of Sozuer et al. is that clustering is applied separately on each network node. Thus, generalizations for multiple nodes cannot be found. In another study, Hashmi et al. [37] used various clustering and outlier detection techniques to analyze one year of network failure data collected from a national broadband network. Each failure was analyzed with respect to five attributes: fault occurrence date, time of the day, geographical region, fault cause (from 92 possible causes), and resolution time. Using clustering (k-means, fuzzy c-means or self-organizing maps), interesting spatio-temporal clusters of faults were found. For example, one cluster indicated that fault resolution typically takes a long time between 1 and 4 PM in a specific region for some fault types. Such insights can be useful to improve service in that area. Moreover, anomaly detection techniques were applied (either local outlier factor or local outlier probabilities) to identify abnormal data points. For example, some detected anomalies were faults occurring during the night [37]. In experiments, the sum of squared errors (SSE) and Davies-Bouldin index (DBI) values were used to evaluate the performance of different techniques. Note that the DBI is the average ratio of intra cluster variance and inter cluster distance of all clusters. Obtaining a low DBI value is desirable because it indicates that there is a high separation between clusters. The results indicated that the k-means clustering method outperformed fuzzy c-means clustering. In fact, the k-means clustering algorithm was able to create a larger separation between different clusters which improved the accuracy by obtaining clusters that are very different from each other. Moreover, experiments have shown that clustered self-organizing maps outperformed k-means and fuzzy c-means in terms of both SSE and DBI.

3.4 Summary and Perspective This section has reviewed some key approaches for fault management using pattern discovery algorithms. The reader can refer to Table 1 for a summary of these approaches. The main benefit of using a pattern mining approach is that patterns

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Table 1 Summary of reviewed pattern mining-based approaches Study

Category

[38, 48] Episode mining

Input

Output

Event Episode rules, sequence association extracted from rules alarms data records

Relationship graph is extracted from frequent sequential patterns

Observation

Measure

Dataset

(1) Not Support, suitable for confidence long event sequences (2) The number of generated rules is large

Several real-world datasets from telecommunication companies

The graph Support, relationship is confidence difficult to extract and to use by experts in case of large alarm datasets

Dataset triggered in a 3G mobile network

6 alarms datasets with several network domains: 2G,3G,4G, CS and PS

[56]

Sequential Alarm pattern mining sequence (PrefixSpan) database extracted using silent periods as delimiters

[74]

Sequential Alarm Flapping rules, pattern mining sequence Parent–Child (PrefixSpan) database rules extracted using occurrence number of alarms with time window

a P 2 lookup table is required for each network domain which is not easy to create

[19]

Sequential Alarm pattern mining sequence (GSP) database extracted using a sliding window

Reinforcement Support, learning is confidence, lift performed to filter unimportant rules

Real-world dataset containing alarms from a Portuguese telecommunications company

[69]

Clustering (K-means)

Alarm Clusters of sequence alarms database extracted using the life cycle of alarms

The clustering method is applied only on data of each node separately.

Term frequency (TF), inverse item frequency (ITF)

Alarms data extracted from a Nokia radio access network logs

[37]

Clustering (various)

Network failure log

Can discover spatiotemporal clusters of failures and anomalies

Various

Data from a national broadband provider

Association rules extracted from frequent sequential patterns

Clusters of failures and anomalies

Support

found can be easily understood by humans. There are many interesting possibilities for carrying further research on pattern mining for fault management. The next paragraphs discusses some of these possibilities. First, it is observed that most of the above approaches are designed to handle rather simple data types (mostly discrete sequences) where alarms are viewed as events that have some attribute values. It would be interesting to consider more

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complex data representation to extract richer patterns. For instance, none of the above studies consider the spatial dimension (the network topology) in the pattern mining process. An interesting possibility is to view the network as a dynamic graph where alarms are spreading along edges (communication links) between vertices (network devices) to find spatio-temporal patterns. To extract such patterns, graphbased pattern mining algorithms could be considered or extended. For example, there exists several algorithms for discovering patterns in dynamic attributed graphs. A dynamic attributed graph is a graph where vertices may be described using multiple attributes, and the graph evolves over time (edges and vertices may be added or deleted, and attribute values may change) [20, 25]. A network can be modeled as such a graph, where node attributes can be used to represent alarms. Then, various types of patterns could be discovered to reveal relationship between alarms such as (1) Cohesive co-evolution pattern [20] (a set of vertices that are similar and display the same trends for some attribute(s) during a time interval), (2) Triggering patterns [45] (a rule of the form L → R where L is a sequence of attribute variations followed by a single topological change, R), and significant trend sequences [25] (correlated attribute variations for connected nodes). Another possibility is to use other time representations for the input data as well as for patterns found. For example, while most of reviewed studies consider a strict sequential ordering between events in patterns, some algorithms have been designed to extract partial orders (patterns where events are partially ordered) [23, 65]. It is possible to consider richer relationships between events by explicitly representing each event as a time interval [18]. Another possibility is to consider extensions of traditional frequent pattern mining algorithms. For example, some extensions were proposed to handle items (events) having weights indicating their relative importance [53], weights and quantities [29, 72, 73], and cost values [27], as well as to use fuzzy functions [54] and taxonomies of items [12, 30, 82]. Using such algorithms would allow to consider richer information. Another research direction is to explore the use or development of appropriate techniques for visualizing alarm patterns. For example, Jentner and Keim presented a detailed survey of many visualization techniques for patterns [41]. An important issue that could be also studied is how to reduce the number of patterns presented to the user by selecting the most important patterns or summarizing these patterns by reducing redundancy. There are several studies on this direction in the field of pattern mining such as to mine concise summary of patterns such as closed patterns [35, 51], maximal patterns [31, 58], and generator patterns [26, 83]. Another research direction is to go beyond the classical measures to select patterns such as the support and the confidence. A well-known problem with the support measure is that frequent alarms are sometimes unimportant and a limitation of the confidence is that it is very sensitive to the frequency of a rule’s consequent in a database. Thus, other measures could be considered such as the lift [11], and application-specific measures could be designed. Lastly, it is possible to build upon the research on stream pattern mining to adapt the current approaches for real-time processing [15, 61].

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4 Machine Learning-Based Approaches Apart from pattern mining-based techniques, several studies have applied machine learning techniques for network fault management. The next subsections review studies that have used different types of machine learning techniques: (1) artificial neural networks, (2) decision tree learning, (3) Bayesian networks, (4) SupportVector Machines, (5) dependency graphs, and (6) other approaches.

4.1 Artificial Neural Networks-Based Approaches Artificial Neural networks (ANN) are one of the most popular types of machine learning models [52, 55]. Their structure and learning mechanisms are loosely inspired by the human brain. Over the years, multiple ANN architectures have been proposed for different needs. A traditional feed-forward ANN contains multiple layers of neurons that are interconnected, where some neurons receive some numeric values as input, while others output numeric values. An ANN is typically trained in a supervised manner using some examples of inputs and corresponding desired outputs. After training, a neural network can predict output values for new input values. Some key properties of artificial neural networks is that they can approximate various nonlinear functions (mapping inputs to outputs), and that they are resilient to noise [21, 71]. Because of these properties, artificial neural networks are suitable for fault management. Wietgrefe et al. [77] presented an artificial neural network-based alarm correlation system for correlating alarms in a GSM network. The proposed system is called Cascade Correlation Alarm Correlator (CCAC). It relies on an ANN to find the causes of alarms. Each input neuron of the ANN represents an alarm type and takes a binary value (the alarm is active or inactive), while each output layer neuron correspond to a problem’s cause. The neural network is trained using sets of alarms with their known causes. During the training, weights of an hidden layer of neurons are adjusted. Then, after training, the neural network can be fed with alarm data to obtain the likely alarm causes. It was demonstrated that CCAC works well even in the presence of noisy data, where noise is defined as some missing alarms or some additional irrelevant alarms. In a comparative study done by Wietgrefe [76], it was found that the trained ANN is more accurate at finding alarm causes than several other approaches such as case-based reasoning and rule-based reasoning approaches. A limitation of this study is that it does not consider the temporal relationships between alarms, and how devices are interconnected. To take the time ordering of alarms into account, Marilly et al. [60] proposed an hybrid approach for fault management that combines a multi-layer feed-forward ANN with signal-processing techniques. This method receives as input an alarm log and first removes redundant alarms. Then, each alarm is feed to the input layer of the ANN. Each input neuron represents either an alarm type or a logical network entity. These inputs then pass through two hidden layers to produce an output (an alarm class). After repeating this process for each alarm, a sequence of alarm classes

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is obtained ordered by time, which forms a signal. Then, the signal is treated using signal-processing methods such as a time-frequency visualization to extract pertinent information that could help identifying the cause of the malfunction (e.g., peaks of alarms that could indicate an equipment breakdown). This approach is robust to noise but still requires that an expert intervenes to identify malfunction in signals. In another study, Arhouma and Amaitik [5] have compared different types of ANN models for alarm correlation in the ALMADAR GSM network by changing parameters of a neural network such as the network type, number of hidden neurons, and the learning algorithm. As in the work of Wietgrefe et al. [77], input neurons represent alarms while output neurons represent an initial cause of failure. It was found that the cascade-forward network type with Levenberg–Marquardt back-propagation training function gave the best diagnoses. Differently, from the above studies, Barreto et al. [7] designed a fault detection approach to monitor the condition of a cellular network and detect abnormal behaviors. In this approach, the current state of a network is described as a vector of KPI (key performance indicator) values. Then, an ANN is trained with normal and abnormal states to then be able to classify a novel network state as abnormal or not. In that study, various neural network types were compared such as Self-Organizing Maps (SOM) and Neural Gas using a simulator. In summary, although neural network-based approaches have the ability to extract patterns from complex and noisy data, these methods sometimes have a long training time and have difficulty to predict correctly for data that is largely different from the training data [21, 71]. Another issue with neural network-based approaches is that it is sometimes difficult to understand their inner-working.

4.2 Decision Tree-Based Approaches Another machine learning technique that has been used for fault management is decision tree learning. It is a supervised learning method, which requires to provide a set of training instances described using some attributes and where an attribute is selected as the target attribute to be predicted. Each possible value for the target attribute is called a class. From the training data, a decision tree is built using a learning algorithm. Then, the model (tree) can be used to classify (predict the class) new instances (data records). A decision tree is a tree-like structure that is designed to support the classification of data instances based on their attribute values. In a decision tree, each leaf represents a decision (a class), while internal nodes represent a test of an attribute and each outgoing branch from an internal node represents a possible value of this attribute. An instance can be classified using a decision tree by traversing it from the root node and following the branches based on the instance’s attribute values until a leaf node is reached [14]. Chen et al. [17] proposed a decision tree based method for failure diagnosis in large Internet systems. A decision tree was trained to classify the failed and successful

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requests occurring during faulty periods. In the tree, the leaf nodes indicate whether a request is successful or has failed while internal nodes represent different system features such as a Machine’s name and the name of a software program running on that machine. To identify causes of failures, post-processing is performed on paths of the generated decision tree by ranking them based on their correlation with failures. At this point, important features that correlate with the largest number of failures are selected. Kiciman and Fox [47] have proposed a system for automatic fault detection in Internet services in which runtime paths of different requests served by the system are studied to identify the system’s faulty components. A runtime path is a sequence of components, resources, and control-flow used to service a request. Besides, after detecting and recording the anomalous or successful requests with their corresponding paths, a decision tree is used to identify the components that may cause the failures. More precisely, a decision tree classifies the different paths into anomalous or successful paths based on their properties. Then, from the decision tree, a set of rules are extracted and used to extract the software and hardware components that lead to the failures. Apart from fault localization in computer networks, decision trees have also been used for fault localization in other domains such as speech recognition [14] and Enterprise software systems (ESS) [67]. A drawback of decision tree based approaches for fault localization is that they may suffer from a degraded accuracy when dealing with noisy data [21, 71].

4.3 Bayesian Networks-Based Approaches Bayesian networks are a type of Probabilistic Graphical Models that can be used to build models from data or from expert opinions. Bayesian networks are directed acyclic graphs (DAG) in which nodes correspond to random variables over a multivalued domain while edges represent the causal relationship between nodes which is measured by the conditional probability [21]. Bayesian networks can be used to deal with a wide range of tasks such as prediction, decision under uncertainty, and diagnosis [13]. Barco et al. [6] proposed a model based on discrete Bayesian networks, namely smooth Bayesian network, for automatic fault identification in cellular networks. The main purpose of this model is to improve the fault identification process by decreasing the sensitivity of diagnosis accuracy which happens principally due to the imprecision of the model’s parameters. In other words, the model was designed to improve the diagnosis accuracy by overcoming the inaccuracy of a model’s parameters. The proposed approach considers alarms and key performance indicators registered daily by the network management system for fault identification. Experimental results on data from GSM/GPRS networks have shown that the proposed smooth Bayesian network outperforms traditional Bayesian networks in case of inaccuracy of the model’s parameters.

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In another study, Ruiz et al. [68] used a Bayesian Network to identify the causes of network failures at the optical layer, and give them probabilities. The input of the Bayesian network is monitoring data represented as two time series about bit error rates and received power. The data was discretized to train the Bayesian network. Then, it can predict two types of failures, namely inter-channel interference and tight filtering. The approach was found to provide high accuracy in a simulated environment. Khanafer et al. [46] developed a Bayesian network-based fault isolation approach for Universal Mobile Telecommunications System (UMTS) networks. Given some symptoms (KPIs and alarms), the system can predict the cause. Because data about symptoms is continuous, Khanafer et al. first discretized the data (using two methods, namely percentile-based dicretization and entropy minimization discretization). This allowed to automatically find thresholds for symptoms that may indicate faults. Then, a Naive Bayes network was built in which the conditional probabilities linking causes to symptoms were learnt from training data based on the thresholds. Experiments on data from a real UMTS network have shown that the proposed approach identified the correct cause of problems 88% of the time. To handle time, Ding et al. [42] proposed an approach for fault management in IP networks, which relies on a dynamic Bayesian network. In that network, dependencies between network objects (e.g., devices, software processes) are modeled, and how they evolve over time with conditional probabilities. The dynamic Bayesian network can be used to predict the state of a network object and the evolution of dependencies between two objects. Moreover, it can also be used to infer the likely causes of some observed symptoms using backward inference. This study focused on faults caused by soft changes (changes that gradually occur over time) in a network, and their causes. The approach was only tested with simulated data. Besides fault isolation, Bayesian networks were also used to predict faults in communication networks [2, 39, 49] to perform pro-active maintenance. The above studies have shown promising results but Bayesian networks have some limitations. One of them is that several Bayesian models rely on some assumptions of independence between some events. Another is that a considerable amount of training data is required to correctly estimate conditional probabilities. A third one is that complex temporal relationships between alarms and interactions between devices are difficult to model using Bayesian Networks.

4.4 Support-Vector Machine-Based Approaches Another popular machine learning technique that is used for fault management is support-vector machine (SVM). It is a supervised technique that is generally used for classification or regression analysis. SVM is a linear classifier which is based on the margin maximization principle [1]. Given labeled data as input, the main idea of SVM is to find an optimal separating hyperplane that divides the input data into two classes. Note that to deal with non-

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linear problems, the data can be mapped to higher dimensions to find a separating hyperplane. This mapping is performed using kernel methods and this process is called nonlinear SVM [4]. Wang et al. [75] have combined SVM with double-exponential smoothing (DES) to predict optical network equipment failures. To perform this task, some selected indicators are used as features to predict equipment failures. If an indicator is related to equipment failure, a change of this indicator’s value will directly affect the equipment’s state. Besides, since the relation between different indicators and equipment failures is not linear, a kernel function with punishment vector needs to be selected. This selection is performed by trying multiple kernel functions with different punishment factor values on SVM, using tenfold cross-validation to calculate their accuracy. Then, the combination that yield the highest accuracy is selected. Before applying SVM, DES is used to predict the value of each indicator at time (t + T ) taking the historical data from time (t − n) to (t − 1). At this point, the SVM method is applied to predict equipment failures at time (t + T ). Note that (t − n) to (t − 1) is a period of time that is selected in terms of days in the experiment and (t + T ) is the next period of time from the end of the previous time period to the end of the whole observation period. It was found that this improved SVM method can achieve an average of 95% accuracy (it can predict 95% of equipment failures). In another study, Yuan et al. [85] proposed a system to automatically identify the root causes of problems in computer systems based on low-level traces of their behavior. This approach is different from that of utilizing a text-based search to find solutions to problems, which has been used in other systems. The proposed system has two main components: the tracer and the classifier. The tracer collects the list of events that occur in the system when a problem’s symptom is reproduced. Besides, the tracer records most system calls, which have several attributes such as Sequence number, Process ID, and Thread ID. After collecting all system call sequences related to symptoms, the system extracts n-grams from them. An n-gram can be viewed as sequential pattern where events must be consecutive. Then, the system encodes each log sequence as a bit vector where each dimension indicates the presence or absence of an n-gram. At this point, the SVM classifier is applied on the set of bit vectors generated in the previous step for predicting the root cause of new traces from the previous registered traces with their known root causes. Besides, to prevent over-fitting due to the limited data, k-fold cross-validation is applied. It divides the training data into k partitions and then repeat selecting one partition to test it with the classifier trained with the remaining data until all data has been used for testing. The proposed approach was evaluated using four case examples containing diverse root causes. A prediction accuracy of nearly 90% was obtained. Zidi et al. studied fault management for Wireless Sensor Networks (WSNs). A WSN is a set of autonomous devices that collaborate together through a wireless channel. WSNs are used to collect, process, and send data in various situations. However, WSNs may suffer from numerous failures [88]. Hence, Zidi et al. [88] proposed a new SVM-based technique for failure detection in WSNs. The proposed approach has two phases: The first phase is performed on anticipated time; whereas,

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the second one is performed on real time. The first phase is the learning phase where the main objective is to obtain a decision function from learning data using SVM. Besides, data is composed of a set of normal data as well as a set of faulty data. Then, the decision function is further used in the second phase to detect in real time whether a new observation is normal or belongs to faulty cases. To evaluate the performance of the proposed method, 21 datasets were formed from a previously published database. The collected datasets are composed of a set of sensor measurements where different types of faults are injected with certain degrees. The experimental results show that the proposed method achieves high detection rate (99% in most cases).

4.5 Dependency Graph-Based Approaches These approaches are based on the construction of a dependency graph to represent different network elements. In [44], a graph-based method is developed for the fault localization problem. Besides, a dependency graph is constructed for the different network objects. Each vertex in the graph dependency is assigned with a weight which represents the probability that this object fails (triggers alarms) independently of other dependent network elements. On the other hand, each directed edge between two vertices is assigned with a weight that represents the strength of dependency between these vertices. In other words, the assigned weight is the conditional probability that the failure of one object is due to the failure of the other object. These weights can be estimated from the system specification information or from the history of previous failures [44]. After the graph construction, the system finds the domain of each alarm in the system which is defined as the set of objects that can cause this alarm. Note that, the problem of finding alarm domains is formulated as a variant of single source problem. At this point, a set of localization algorithms are used to discover alarm correlation patterns and identify fault locations. Bouillard et al. [9, 10] have proposed another graph dependency based approach to correlate alarms in a network. This method is based on the assumption that frequently occurring alarms just refer to general information about the system, while rare alarms are viewed as more important since they may reveal a critical problem in the system. Accordingly, this method focuses on observing rare alarms. First, this approach calculates the most frequent alarms. Then, based on these alarms, the alarm sequence is cut into set of small patterns (set-patterns) where frequent alarms are used as separators. Then, set-patterns are reduced using some transformation rules to facilitate their analysis. At this point, the dependency graph from the reduced sets pattern is constructed and is divided into a set of subgraphs where each subgraph focuses on one rare alarm or a set of rare alarms. Finally, these subgraphs are further analyzed by network experts to discover the root cause of these alarms.

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4.6 Other Approaches Several of the reviewed approaches are passive. Johnsson and Meirosu [43] proposed an active fault management approach to perform fault localization in a packet-switched network. In that study, a network is viewed as an undirected graph connecting devices, and a fault is a performance degradation such as a large packet loss rate or transmission delay. Periodically, packets are sent between pairs of device to evaluate the network performance. They collected information about delays, jitters, and errors for the different edges of this graph and then used to calculate the probability that an edge is the source of a fault. The proposed approach utilizes probabilistic inference with a discrete state-space particle filter (also called histogram filter) to calculate the most probable location of a fault. This approach is lightweight and is applied in real time. The approach was evaluated using a simulator.

4.7 Summary and Perspective This section has reviewed several studies, which have applied machine learning for fault management. Table 2 provides a summary of the main reviewed approaches discussed in this section. Most of the approaches surveyed in this section can be viewed as supervised approaches (requiring training data). There are many possibilities for future work about using machine learning techniques. First, only a handful of machine learning techniques have been used for fault management, and there has been many advances in this field in recent years. Thus, newer techniques may be considered such as deep learning models [52], which may provide better results. Second, it would be interesting to explore using models that consider richer information. For example, some models designed to handle temporal information could be used such as LSTM (Long Short Term Memory) [36]. Finding a way of also considering the network topology could lead to interesting results. Third, the use of larger datasets with more features could be helpful to train better models. Semi-supervised machine learning techniques could also be used to reduce the need for human intervention. Synthetic but realistic data could also be generated to increase the size of the training data. Fourth, an interesting alternative to passive monitoring (where a network management system waits passively for alarms sent by network nodes) is active monitoring (where the management system probes each nodes to verify its state). The advantage of active monitoring is that it may help recovering from faults more quickly. However, designing active monitoring techniques brings more challenges for network management [21]. Fifth, we have noticed that there are some recent studies on the use of machine learning in general and neural networks in particular for LTE and 5G networks [16, 22]. However, to the best of our knowledge, few studies have applied neural

Category

Neural network

Neural network

Neural network

Neural network

Decision tree

Decision tree

Bayesian network

Bayesian network

Bayesian network

Study

[77]

[60]

[5]

[7]

[17]

[47]

[6]

[68]

[46]

Output

Each output neuron represents an initial cause of failure

An output neuron represents an alarm class

Each neuron of the output layer represents an initial cause of failure

KPIs and alarms

Time series about received power and error rates

Causes and symptoms (KPIs, alarms)

A set of runtime paths with their associated features

A set of vectors corresponding to request paths with their associated features

A cause of failure

One of two cause of failure

The conditional probabilities of fault causes for the observed symptoms

A set of rules extracted from a decision tree’s paths

A set of paths extracted from a decision tree

A vector of KPIs The state is abnormal or not representing a network state

Each input neuron represents an alarm

Each input neuron represents a network’s logical entity or alarm type from alarm log

The input layer is a set of binary alarm vectors where each alarm is represented by a neuron

Input

Table 2 Summary of machine learning-based approaches Observation

Dicretization methods were employed to find thresholds for continuous variables

The model has good accuracy but consider only two causes

In experiments, the diagnostic model does not take alarms into consideration

The decision tree is a part of a pinpoint approach that is applied without requiring any apriori information from experts

Leaf nodes that contain less failure requests will be ignored in the diagnosis process

The neural network is applied separately to each network subarea. Small training set (55 patterns)

The neural network’s output classes must be predefined by a network operator (not an easy task)

The algorithm was found to tolerate well noisy data but only a few alarm vectors were used

Algorithm

Naive Bayes model

Bayesian network

Naive Bayes model

ID3

C4.5

SOM, Net-Gas and others

Cascade-forward ANN

Multi-layer feed-forward ANN

Cascade Correlation Algorithm

Dataset

(continued)

UMTS network data

Simulated data

3 months of data from a GSM/GPRS network

A testbed is deployed with three Internet services that include a wide variety of failures

Months of data from eBay’s production website

Data from simulator

Subparts of real GSM mobile network

Small set of alarm logs of an SDH network

Subset of 94 alarms chosen from real network

A Survey of Machine Learning for Network Fault Management 21

Category

SVM

SVM

SVM

Dependency graph

Dependency graph

Dependency graph

Other

Study

[75]

[85]

[88]

[9]

[9]

[44]

[43]

Table 2 (continued)

Input

Output

Predict the class failure of new traces

KPIs about packet transmission on different links

Network elements are modeled as a dependency graph

An event sequence divided into small pattern-sets using frequent alarms as delimiters

An event sequence divided into small pattern-sets using frequent alarms as delimiters

The network link that is likely faulty

Discover the number of fault hypotheses and assign a confidence measure to each one

The dependency graph is divided into small subgraphs. Conditional probabilities are used to find root causes

The dependency graph is divided into small subgraphs. Conditional probabilities are used to find root causes

A set of observation vectors Detection of faulty data where each vector has 12 dimensions. Each dimension represent a sensor measurement and these sensor measurements are taken in 3 consecutive instances

System call sequences

A set of vectors, each Predict if the equipment vector is composed of set of will fail indicator values

Observation

A fault is a performance degradation. Considers the network topology. Can be applied in real-time

This method was not tested on a real network

This method is based on the assumption that rare alarms can give information about failure sources

This method is based on the assumption that rare alarms can give information about failure sources

The faulty data are injected by authors with different rates of faults and different types of faults. This method allows to detect faults in real time. However, it cannot predict faults before its occurrence to prevent it

To perform a prediction, it is necessary to reproduce the problem’s symptoms, which is not always convenient

Tenfold cross-validation is used twice for selecting relevant indicators to failures as well as for selecting the best failure diagnostic model

Algorithm

Probabilistic inference

Dependency graph

Dependency graph

Dependency graph

Non-linear SVM classifier

Linear SVM classifier

double-exponential smoothing with Support-Vector Machine

Dataset

Simulated data

Simulated network with 50 nodes, where each node has a failure probability

1 year alarm log from two element networks of the Alcatel-Lucent operator

1 year alarm log from two element networks of the Alcatel-Lucent operator

A set of 21 labeled WSNs datasets, each dataset consists of a set of sensor measurements where different type of faults are injected

Collect event traces of four computer problems occurring in the Windows XP SP2 system

44 d of real data collected in a WDM network from a telecommunications operator

22 M. Nouioua et al.

A Survey of Machine Learning for Network Fault Management

23

network in LTE and 5G networks for fault management [24, 62]. Thus, exploiting recent advances in machine learning in general and neural network in particular for fault management in 5G and LTE networks is a promising direction for future work. Another research direction is to develop hybrid machine-learning based systems for fault management, which is to combine different machine learning techniques in a complementary way to efficiently perform fault management, while overcoming the disadvantages of using only one machine learning technique [13]. Moreover, with large and complex networks, both time and complexity become very large which makes the computation intractable [21]. As a result, developing techniques to reduce runtime and memory requirements of models while preserving their accuracy is crucial. Finally, it is worth noticing that fault management in computer networks is a very active research topic. The reason is that, even with the development of many techniques based on pattern mining and machine learning, novel networking technologies and applications raise new challenges. Hence, existing systems become insufficient and must be improved [78].

5 Conclusion This chapter has presented a survey of the main studies on using data mining and machine learnin- based techniques for network fault management, including a description of their characteristics, similarities, differences, and shortcomings. This is an active research area with many research opportunities.

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Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes Segun I. Popoola, Ruth Ande, Kassim B. Fatai, and Bamidele Adebisi

Abstract Bidirectional gated recurrent unit (BGRU) can learn hierarchical feature representations from both past and future information to perform multi-class classification. However, its classification performance largely depends on the choice of model hyperparameters. In this paper, we propose a methodology to select optimal BGRU hyperparameters for efficient botnet detection in smart homes. A deep BGRU multi-class classifier is developed based on the selected optimal hyperparameters, namely, rectified linear unit (ReLU) activation function, 20 epochs, 4 hidden layers, 200 hidden units, and Adam optimizer. The classifier is trained and validated with a batch size of 512 to achieve the right balance between performance and training time. Deep BGRU outperforms the state-of-the-art methods with true positive rate (TPR), false positive rate (FPR), and Matthews coefficient correlation (MCC) of 99.28 ± 1.57%, 0.00 ± 0.00%, and 99.82 ± 0.40%. The results show that the proposed methodology will help to develop an efficient network intrusion detection system for IoT-enabled smart home networks with high botnet attack detection accuracy as well as a low false alarm rate. Keywords Botnet detection · Internet of Things · Smart homes · Deep learning · Gated recurrent unit

S. I. Popoola (B) · R. Ande · B. Adebisi Department of Engineering, Manchester Metropolitan University, Manchester, UK e-mail: [email protected] R. Ande e-mail: [email protected] B. Adebisi e-mail: [email protected] S. I. Popoola · R. Ande · K. B. Fatai Artificial Intelligence for Cybersecurity Research, Cyraatek, Manchester, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_2

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1 Introduction Embedded devices are interconnected to each other and further connected to the Internet to form an Internet of Things (IoT) [1, 2]. Smart home paradigm exploits the enormous capabilities of IoT technologies to develop intelligent appliances and applications such as smart televisions, smart fridges, smart lighting, and smart security alarm systems [3, 4]. Primarily, IoT-enabled devices in homes autonomously communicate with residents and other IoT-enabled devices over the Internet. Unfortunately, invalidated assumptions and incompatibility in the integration of multiple IoT technologies, standards, proprietary communication protocols, and heterogeneous platforms have exposed smart homes to critical security vulnerabilities [5]. Most of the IoT devices and applications that are in use today were developed with little or no consideration for cybersecurity [6]. Hence, IoT devices tend to be easier to compromise than traditional computers [7]. Cyber attackers exploit the lack of basic security protocols in IoT devices to gain unauthorized remote access and control over insecure network nodes [8, 9]. Compromised IoT devices (i.e., bots) in smart homes can be connected to a master bot in a remote location. This kind of connection helps hackers to form a coordinated network of bots (botnets) [10, 11]. Botnets launch large-scale distributed denial of service (DDoS) attacks with massive traffic volume [10, 12]. Other botnet scenarios include port scanning, operating system (OS) fingerprinting, information theft, and keylogging [13]. Existing security solutions that are primarily designed for traditional computer networks may not be efficient for IoT botnet detection in smart home scenarios due to the unique characteristics of IoT devices and their systems [13]. Traffic patterns of different botnet attack scenarios can be detected in IoT network traffic data using machine learning (ML) approach. Various shallow learning techniques have been proposed to detect botnet attacks in IoT networks. These include support vector machine (SVM) [13–20], decision trees (DT) [14, 15, 19, 21–25], random forest (RF) [8, 15, 18, 21, 23, 26], bagging [15], k-nearest neighbor (k-NN) [15, 19, 21, 23, 24, 26], artificial neural network (ANN) [22, 25, 27], Naïve Bayes (NB) [22, 23, 25], isolation forest [16], feedforward neural network (FFNN) [18], k-means clustering [28, 29], and association rule mining (ARM) [25]. However, data generation in IoT networks is expected to be big in terms of volume, variety, and velocity [30]. Therefore, machine learning techniques with shallow network architecture may not be suitable for botnet detection in big IoT data applications. Deep learning (DL) offers two main advantages over shallow machine learning, namely, hierarchical feature representation and automatic feature engineering, and improvement in classification performance owing to deeper network architecture. DL techniques have demonstrated good capability for botnet detection in big IoT data applications. State-of-the-art DL techniques for IoT botnet detection include deep neural network (DNN) [14], convolutional neural network (CNN) [8, 31–34], recurrent neural network (RNN) [13, 29], long short-term memory (LSTM) [13, 29, 35, 36], and bidirectional LSTM (BLSTM) [36, 37]. The classification performance

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of DL methods depends on the choice of optimal model hyperparameters. However, model hyperparameters are often selected by trial and error methods in previous studies. Gated recurrent unit (GRU) is a variant of RNN, and it is suitable for large-scale sequential data processing [38]. Bidirectional GRU (BGRU) has a unique advantage of accessing both past and present information to make an accurate decision for efficient classification performance with lower computational demands [39]. To the best of our knowledge, previous researches have not investigated the capability of BGRU for botnet detection in a smart home scenario. In this paper, we aim to find the optimal hyperparameters for efficient deep BGRU-based botnet detection in IoT-enabled smart homes. The main contributions of this paper are summarized as follows: a.

b. c. d.

A methodology is proposed to determine the most suitable hyperparameters (activation function, epoch, hidden layer, hidden unit, batch size, and optimizer) for optimal BGRU-based multi-class classification. A deep BGRU model is designed based on the selected model hyperparameters to distinguish normal network traffic from IoT botnet attack scenarios. The proposed methodology is implemented, and the deep BGRU model is developed with the bot-IoT dataset. We evaluate the performance of the deep BGRU model based on training loss, validation loss, true positive rate (TPR), false positive rate (FPR), Matthews correlation coefficient (MCC), and training time.

The remaining part of this paper is organized as follows: Sect. 2 describes the proposed methodology for selection of optimal BGRU hyperparameters; the method is employed to develop a deep BGRU model for efficient IoT botnet detection; the results of extensive model simulations are presented in Sect. 3, and Sect. 4 concludes the paper.

2 Deep BGRU Method for Botnet Detection in IoT Networks In this section, we describe the concept of BGRU, the proposed methodology for optimal BGRU hyperparameters, and the development of efficient deep BGRU classifiers for botnet detection in the context of a smart home. The overview of the framework is shown in Fig. 1.

2.1 Bidirectional Gated Recurrent Unit GRU is a variant of RNN. This hidden unit achieves performance similar to LSTM with a simplified gated mechanism and lower computation requirements [40]. Unlike

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S. I. Popoola et al.

Fig. 1 Optimal hyperparameters of BGRU

LSTM, GRU discards the memory unit and replaces the input and forget gates with an update gate. Figure 2 shows the standard structure of a GRU. A GRU has two gates, namely, the reset gate (r i ) and the update gate (zi ). These gates depend on past hidden state (ht−1 ) and the present input (x t ). The reset gate determines whether the past hidden state should be ignored or not, while the update gate changes the Fig. 2 The architecture of GRU

Deep Bidirectional Gated Recurrent Unit …

33

past hidden state to a new hidden state (h˜ i ). The past hidden state is ignored when ri  0 such that information that is not relevant to the future is dropped and a more compact representation is obtained. The update gate regulates the amount of data that is transmitted from the past hidden state to the present hidden state. GRU is a unidirectional RNN, i.e., it employs a single hidden layer, and its recurrent connections are in the backward time direction only. GRU cannot update the present hidden state based on the information in the future hidden state. Interestingly, BGRU updates its current hidden state based on both the past and the future hidden state information [41]. A single BGRU has two hidden layers, which are both connected to the input and output. The first hidden layer establishes recurrent connections between the past hidden states and the present hidden state in the backward time direction. On the other hand, the second hidden layer establishes recurrent connections between the present hidden state and the future hidden states in the forward time direction. The computation of BGRU parameters is obtained by (1)–(9): ← r− i =σ





   ← ← ← + U r h(t−1) + br ,



Wr x 



zi = σ





i









i

+ U z h(t−1)

Wzx i

 + bz , ←

(1) (2)

i

      ← − ← ← ← ← h˜ i(t) = φ W x + U r  h(t−1) , i

(3)

i

←− ← − ←←(t−1)  ← h i(t) = z h i + 1 − z i h˜ i(t) ,

(4)

   (t+1) + br , rh + U

(5)

   (t+1) + bz ,  zh + U

(6)

     −→  (t+1) x + U  r  h h˜ i(t) = φ W ,

(7)

−→ −→

→ → h i(t) = − z i hi(t−1) + 1 − − z i h˜ i(t) ,

(8)

  ←− −→ (t) y˜i = ϑ W y h(t) h + U + b y t y , t

(9)

− → ri = σ − → zi = σ

 

 rx W  zx W

i

i

i

i

i

i

where x, r, z, h, y˜ and i are the input, reset gate, update gate, hidden state, output, ← − − → and hidden unit index, respectively; (·) and (·) represent the parameters of the hidden layers in the backward and forward time directions, respectively; W (·) and U (·) are

34

S. I. Popoola et al.

the weight matrices while b(·) is the bias vector; σ (·) is a logistic sigmoid activation function; φ(·) is either hyperbolic tangent (tanh) or rectified linear unit (ReLU) activation function; and ϑ(·) is a softmax activation function.

2.2 The Proposed Method for Selection of Optimal BGRU Hyperparameters The proposed method for optimal selection of BGRU hyperparameters is presented in Algorithm 1. Network traffic features are considered as sequential data given by (10): ⎡

x1,1 ⎢ X = ⎣ ... xδ,1

x1,2 · · · .. . . . . xδ,2 · · ·

⎤ x1,μ .. ⎥ . ⎦

(10)

xδ,μ

where μ is the number of network traffic features, and δ is the number of network traffic samples. The network traffic features in the training, validation, and testing sets are represented by X tr , X va , and X te , respectively. The ground truth labels for training, validation, and testing are represented by ytr , yva , and yte , respectively. The selection of optimal BGRU hyperparameters will lead to efficient detection and classification of IoT botnet attacks. These hyperparameters include activation function (af ), epoch (ep ), hidden layer (hl ), hidden unit (hu ), batch size (bs ), and optimizer (op ). The optimal choice is made from a set of commonly used hyperparameters through extensive simulations. The collection of the hyperparameters is given by (11)–(16):   a f = a f,1 , a f,2 , . . . , a f,n ,

(11)

  e p = e p,1 , e p,2 , . . . , e p,m ,

(12)

  hl = h l,1 , h l,2 , . . . , h l,k ,

(13)

  h u = h u,1 , h u,2 , . . . , h u,q ,

(14)

  bs = bs,1 , bs,2 , . . . , bs,v ,

(15)

  o p = o p,1 , o p,1 , . . . , o p,g ,

(16)

Deep Bidirectional Gated Recurrent Unit …

35

where n is the number of activation functions in af ; m is the number of epochs in ep ; k is the number of hidden layers in hl ; q is the number of hidden units in hu ; v is the number of batch sizes in bs ; and g is the number of optimizers in op . The default hyperparameters are the first elements in each of the sets. Algorithm 1. Optimal BGRU hyperparameter selection method Input: Initialization: Output: 1. def 2. 3. 4. 5. 6. return to 7. for 8. to 9. for 10. to 11. for 12. to 13. for 14. to 15. for 16. to 17. for 18.

The development of the BGRU model for efficient IoT botnet detection in smart homes involves two main processes, namely, the choice of suitable deep network architecture and loss minimization through model training and validation. Deep network architecture (N) for BGRU is determined by af,c , hl,d , and hu, j. The selected BGRU architecture is trained with X tr , ytr , X va , yva , ep,α , bs,β , and op,γ using backpropagation through time (BPTT) algorithm [42]. Loss minimization during training and validation is assessed based on the values of training loss (ltr ) and validation loss (lva ). A categorical cross-entropy loss function was used for loss minimization in a multi-class classification scenario. The performance of BGRU classifier is based on TPR, FPR, and MCC when evaluated with highly imbalanced testing data (X te , yte ). The definition of these performance metrics is given by (17)–(20) [43]:

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S. I. Popoola et al.



TPR =

TP TP + FN

(17)

FPR =

FP , FP + TN

(18)

 TP + FN λ=2 −1 TP + FN + FP + TN    1 TPR + TNR − 1 +1 ,

MCC = 2 TPR + (1 − TNR) 1−λ 1+λ

(19)

(20)

where true positive (TP) is the number of attack samples that are correctly classified; false positive (FP) is the number of normal network traffic samples that are misclassified as attacks; true negative (TN) is the number of normal network traffic samples that are correctly classified; false negative (FN) is the number of attack samples that are misclassified as normal network traffic; λ is the class imbalance coefficient. For each of the simulation scenarios, an optimal BGRU hyperparameter is expected to produce the lowest ltr , l va , and FPR as well as the highest TPR and MCC.

2.3 Deep BGRU Classifier for IoT Botnet Detection Bot-IoT dataset [13] is made up of network traffic samples that were generated from real-life IoT testbed. The testbed is realistic because IoT devices were included. These IoT devices include a weather station, a smart fridge, motion-activated lights, a remote-controlled garage door, and an intelligent thermostat. This network of IoT devices is considered to be a good representation of an IoT-enabled smart home. The Bot-IoT dataset also contains recent and complex IoT botnet attack samples covering five common scenarios, namely, DDoS, DoS, reconnaissance, and information theft. Accurate ground truth labels are given to the IoT botnet attack samples. The samples of DDoS attack, DoS attack, normal traffic, reconnaissance attack, and information theft attack in the Bot-IoT dataset are 1,926,624, 1,650,260, 477, 91,082, and 79, respectively. Network traffic samples, IoT botnet attack samples, and ground truth labels were pre-processed into appropriate formats suitable for deep learning. First, the complete dataset was randomly divided into a training set (70%), validation set (15%), and testing set (15%) as suggested in the literature [44, 45]. Non-numeric elements in feature matrices (X tr , X va , X te ) and label vectors (ytr , yva , yte ) were encoded using integer encoding and binary encoding methods, respectively. Furthermore, the elements of feature matrices were transformed using a min–max normalization method such that the value of each element falls between 0 and 1 [46].

Deep Bidirectional Gated Recurrent Unit …

37

The proposed method for the selection of optimal BGRU hyperparameters was implemented. All simulations were performed at a learning rate of 0.0001. The default hyperparameters for the simulations were the ReLU activation function, five epochs, a single hidden layer, 200 hidden units, a batch size of 128, and Adam optimizer. We investigated the suitability of the following: tanh and ReLU activation functions; epochs of 5, 10, 15, and 20; hidden layers of 1, 2, 3, and 4; hidden units of 10, 50, 100, 150, and 200; batch sizes of 32, 64, 128, 256, and 512; and finally, the optimizers (Adam, SGD, RMSprop, and Adadelta). Model training, validation, and testing were implemented using Keras library developed for Python programming running on Ubuntu 16.04 LTS workstation with the following specifications: RAM (32 GB), processor (Intel Core i7-9700 K CPU @ 3.60 GHz × 8), Graphics (GeForce RTX 2080 Ti/PXCIe/SSE2), and 64-bit operating system. The optimal BGRU hyper  ˜ ˜ ˜ parameters a˜ f , e˜ p , h l , h u , bs , o˜ p were used to develop a multi-class classifier for efficient IoT botnet detection in smart homes.

3 Results and Discussion In this section, we evaluate the effectiveness of the method proposed for the selection of optimal BGRU hyperparameters in our attempt to develop an efficient IoT botnet detection system for smart home network security. Specifically, we examine the influence of different activation functions, number of epochs, number of hidden layers, number of hidden units, batch sizes, and optimizers on the performance of BGRU-based multi-class classifier.

3.1 Influence of Activation Functions on Classification Performance To determine the right activation function for multi-class classification, ReLU and tanh activation functions were independently employed in two distinct BGRU neural networks, namely, BGRU-ReLU and BGRU-tanh. Apart from the activation function that differs, each of the BGRU neural networks is made up of a single hidden layer with 200 hidden units. BGRU-ReLU and BGRU-tanh were separately trained and validated with five epochs, 128 batch size, and Adam optimizer. Training and validation losses in BGRU-ReLU and BGRU-tanh were analyzed to understand the extent of model underfitting and overfitting, respectively. Figure 3 shows that the ReLU activation function is more desirable than tanh activation function. Generally, training and validation losses reduced in both BGRU-ReLU and BGRU-tanh as the number of epochs increased from 1 to 5. However, training and validation losses were lower in BGRU-ReLU than in BGRU-tanh throughout the 5-epoch period. At the end of the experiment, we observed that training loss in

38

S. I. Popoola et al. 0.3 Tanh (training) Tanh (validation) ReLU (training) ReLU (validation)

0.275 0.25 0.225 0.2

Loss

0.175 0.15 0.125 0.1 0.075 0.05 0.025 0 1

2

3

4

5

Epoch Fig. 3 Training and validation losses of two activation functions

BGRU-ReLU reduced to 0.1000, while validation loss reduced to 0.0721. Also, BGRU-ReLU reduced the average training and validation losses in BGRU-tanh by 31.16% and 35.07%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is minimal when the ReLU activation function was used in BGRU. Model underfitting will lead to poor classification accuracy while model overfitting will adversely affect the generalization ability of BGRU classifier when applied to previously unseen network traffic samples. Consequently, the adoption of the ReLU activation function in BGRU will help to achieve high classification accuracy and good generalization ability required for efficient IoT botnet detection in smart homes. Multi-class classification performance of BGRU-ReLU and BGRU-tanh was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. TPR, also known as sensitivity or recall, is the percentage of samples that were correctly classified; FPR, also known as fall-out or false alarm ratio (FAR), is the percentage of samples that were wrongly classified; and MCC is a balanced measure that accounts for the impact of class imbalance on classification performance. Table 1 shows that BGRU-ReLU performed better than BGRU-tanh. BGRU-ReLU increased TPR and MCC in BGRU-tanh by 24.37% and 25.47%, respectively, while FPR was reduced by 40.86%. The lower the TPR and MCC values, the higher the chances that the BGRU classifier will fail to detect IoT botnet in smart homes. Also, the higher the

Deep Bidirectional Gated Recurrent Unit …

39

Table 1 Performance of BGRU with different activation functions Metric

AF

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev

TPR

tanh

96.55

98.60

63.53

99.90

0.00

71.72 ± 42.85

ReLU

98.01

98.86

74.12

99.99

75.00

89.20 ± 13.38

FPR

tanh

1.33

3.30

0.00

0.00

0.00

0.93 ± 1.45

ReLU

1.08

1.90

0.00

0.00

0.00

0.60 ± 0.87

tanh

98.16

97.34

89.85

99.97

0.00

77.06 ± 43.25

ReLU

98.72

98.38

93.05

99.99

93.30

96.69 ± 3.27

MCC

FPR value, the higher the probability that the BGRU classifier will produce a false alarm, i.e., it is more likely that the classifier will wrongly classify incoming network traffic or IoT botnet attack. Therefore, the choice of the ReLU activation function in BGRU will ensure a high detection rate and reduce false alarm in botnet detection system developed for smart homes.

3.2 Influence of the Number of Epochs on Classification Performance In this subsection, we determine the optimal number of epochs required for efficient BGRU-based IoT botnet detection in smart homes. Four single layers BGRU neural networks were trained and validated with 5, 10, 15, and 20 epochs to produce BGRUEP5, BGRU-EP10, BGRU-EP15, and BGRU-EP20 classifiers, respectively. Each of these classifiers utilized 200 hidden neurons, the ReLU activation function, a batch size of 128, and Adam optimizer. Training and validation losses in BGRU-EP5, BGRU-EP10, BGRU-EP15, and BGRU-EP20 were analyzed to understand the extent of model underfitting and overfitting, respectively. Figure 4 shows that the lowest average training and validation losses were realized with 20 epochs. In general, training and validation losses reduced in all the classifiers throughout the epoch period. However, average training and validation losses were lower in BGRU-EP20 than in BGRU-EP5, BGRU-EP10, and BGRU-EP15. At the end of the experiment, we observed that training loss in BGRU-EP20 reduced to 0.0095, while validation loss reduced to 0.0094. BGRUEP20 reduced the average training losses in BGRU-EP5, BGRU-EP10, BGRU-EP15 by 58.89%, 37.99%, and 17.87%, respectively, while the average validation losses were reduced by 53.80%, 35.22%, and 15.82%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is best minimized when the number of epochs in BGRU was 20. In other words, a sufficiently large number of epochs in BGRU will facilitate high classification accuracy and good generalization ability that are required for efficient IoT botnet detection in smart homes.

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Fig. 4 Mean training and validation loss of different epochs

Multi-class classification performance of BGRU-EP5, BGRU-EP10, BGRUEP15, and BGRU-EP20 was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. Table 2 shows that BGRU-EP20 performed better than BGRUEP5, BGRU-EP10, and BGRU-EP15. BGRU-EP20 increased TPR by 7.97%, 3.65%, and 1.05% relative to BGRU-EP5, BGRU-EP10 and BGRU-EP15, respectively; FPR decreased by 87.27%, 73.08%, and 46.15%, respectively; and MCC increased by Table 2 Performance of BGRU at different number of epochs Metric TPR

FPR

MCC

Epoch

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev 89.72 ± 12.80

5

97.97

99.18

76.47

99.98

75.00

10

99.01

99.60

81.18

99.99

87.50

93.46 ± 8.63

15

99.44

99.90

80.00

99.98

100.00

95.86 ± 8.87

20

99.72

99.94

84.71

99.98

100.00

96.87 ± 6.80

5

0.78

1.94

0.00

0.00

0.00

0.55 ± 0.85

10

0.38

0.95

0.00

0.00

0.00

0.26 ± 0.41

15

0.10

0.54

0.00

0.00

0.00

0.13 ± 0.23

20

0.06

0.27

0.00

0.00

0.00

0.07 ± 0.12

5

98.92

98.43

93.72

99.99

93.30

96.87 ± 3.12

10

99.48

99.24

95.05

100.00

96.77

98.11 ± 2.11

15

99.79

99.60

94.72

99.99

100.00

98.82 ± 2.30

20

99.89

99.80

96.02

99.99

100.00

99.14 ± 1.75

Deep Bidirectional Gated Recurrent Unit …

41

2.34, 1.05, and 0.32%. Therefore, a sufficiently large number of epochs in BGRU will ensure a high detection rate and reduce false alarm in the botnet detection system developed for smart homes.

3.3 Influence of the Number of Hidden Layers on Classification Performance In this subsection, we determine the optimal number of hidden layers required for efficient BGRU-based IoT botnet detection in smart homes. Four BGRU neural networks with 1, 2, 3, and 4 hidden layers formed BGRU-HL1, BGRU-HL2, BGRU-HL3, and BGRU-HL4 classifiers, respectively, when trained using 200 hidden neurons, ReLU activation function, five epochs, a batch size of 128, and Adam optimizer. Training and validation losses in BGRU-HL1, BGRU-HL2, BGRU-HL3, and BGRU-HL4 were analyzed to understand the extent of model underfitting and overfitting, respectively. Figures 5 and 6 show that the lowest training and validation losses were realized with four hidden layers. In general, training and validation losses reduced in all the classifiers throughout the five-epoch period. However, training and validation losses were lower in BGRU-HL4 than in BGRU-HL1, BGRU-HL2, and BGRU-HL3. At the end of the experiment, we observed that training loss in 0.25 Hidden Layer (1) Hidden Layer (2)

0.225

Hidden Layer (3) Hidden Layer (4)

0.2

Training Loss

0.175 0.15 0.125 0.1 0.075 0.05 0.025 0 1

2

3

Epoch Fig. 5 Training loss of different number of hidden layers

4

5

42

S. I. Popoola et al. 0.14 Hidden Layer (1)

0.13

Hidden Layer (2)

0.12

Hidden Layer (3)

0.11

Hidden Layer (4)

Validation Loss

0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1

2

3

4

5

Epoch Fig. 6 Validation loss of different number of hidden layers

BGRU-HL4 reduced to 0.0057, while validation loss reduced to 0.0060. BGRUHL4 reduced the average training losses in BGRU-HL1, BGRU-HL2, and BGRUHL3 by 79.22%, 45.93%, and 13.43%, respectively, while the average validation losses were reduced by 82.44%, 43.88%, and 4.67%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is best minimized when the number of hidden layers in BGRU was 4. In other words, a sufficiently deep BGRU will facilitate high classification accuracy and good generalization ability that are required for efficient IoT botnet detection in smart homes. Multi-class classification performance of BGRU-HL1, BGRU-HL2, BGRU-HL3, and BGRU-HL4 was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. Table 3 shows that BGRU-HL4 performed better than BGRU-HL1, BGRU-HL2, and BGRU-HL3. BGRU-HL4 increased TPR by 9.80, 1.71, and 1.69% relative to BGRU-HL1, BGRU-HL2, and BGRU-HL3, respectively; FPR decreased by 87.27%, 12.50%, and 0%, respectively; and MCC increased by 2.79%, 0.45%, and 0.44%. Therefore, a sufficiently deep BGRU will ensure a high detection rate and reduce false alarm in the botnet detection system developed for smart homes.

Deep Bidirectional Gated Recurrent Unit …

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Table 3 Performance of BGRU at different number of hidden layers Layer

DDoS

DoS

Normal

TPR

1

97.97

99.18

76.47

99.98

75.00

89.72 ± 12.80

2

99.83

99.74

84.71

99.99

100.00

96.85 ± 6.79

3

99.92

99.72

84.71

99.99

100.00

96.87 ± 6.80

4

99.67

99.98

92.94

99.99

100.00

98.51 ± 3.12

1

0.78

1.94

0.00

0.00

0.00

0.55 ± 0.85

2

0.24

0.16

0.00

0.00

0.00

0.08 ± 0.11

3

0.27

0.08

0.00

0.00

0.00

0.07 ± 0.12

FPR

MCC

Reconn.

Theft

Mean ± Stdev

Metric

4

0.02

0.32

0.00

0.00

0.00

0.07 ± 0.14

1

98.92

98.43

93.72

99.99

93.30

96.87 ± 3.12

2

99.78

99.82

96.02

100.00

100.00

99.12 ± 1.74

3

99.78

99.87

96.02

100.00

100.00

99.13 ± 1.74

4

99.90

99.77

98.20

100.00

100.00

99.57 ± 0.77

3.4 Influence of Hidden Units on Classification Performance In this subsection, we determine the optimal number of hidden units required for efficient BGRU-based IoT botnet detection in smart homes. Five single layers BGRU neural networks with 10, 50, 100, 150, and 200 hidden units formed BGRU-HU1, BGRU-HU2, BGRU-HU3, BGRU-HU4, and BGRU-HU5 classifiers respectively when trained using ReLU activation function, five epochs, a batch size of 128 and Adam optimizer. Training and validation losses in BGRU-HU1, BGRU-HU2, BGRU-HU3, BGRUHU4, and BGRU-HU5 were analyzed to understand the extent of model underfitting and overfitting, respectively. Figures 7 and 8 show that the lowest training and validation losses were realized with 200 hidden units. In general, training and validation losses reduced in all the classifiers throughout the five-epoch period. However, training and validation losses were lower in BGRU-HU4 than in BGRU-HU1, BGRUHU2, and BGRU-HU3. At the end of the experiment, we observed that training loss in BGRU-HU4 reduced to 0.0486, while validation loss reduced to 0.0458. BGRUHU5 reduced the average training losses in BGRU-HU1, BGRU-HU2, BGRU-HU3, and BGRU-HU4 by 53.54%, 31.72%, 21.14%, and 11.82%, respectively, while the average validation losses were reduced by 54.83%, 32.94%, 21.85%, and 12.07%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is best minimized when the number of hidden units in BGRU was 200. In other words, a sufficiently large number of hidden units in BGRU will facilitate high classification accuracy and good generalization ability that are required for efficient IoT botnet detection in smart homes. Multi-class classification performance of BGRU-HU1, BGRU-HU2, BGRUHU3, BGRU-HU4, and BGRU-HU5 was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. Table 4 shows that BGRU-HU5 performed

44

S. I. Popoola et al. 0.5 Hidden Units (10) Hidden Units (50) Hidden Units (100) Hidden Units (150) Hidden Units (200)

0.45 0.4

Training Loss

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 1

2

3

4

5

Epoch Fig. 7 Training loss of different number of hidden units 0.3 Hidden Units (10)

0.275

Hidden Units (50) Hidden Units (100)

0.25

Hidden Units (150) Hidden Units (200)

0.225

Validation Loss

0.2 0.175 0.15 0.125 0.1 0.075 0.05 0.025 0 1

2

3

Epoch Fig. 8 Validation loss of different number of hidden units

4

5

Deep Bidirectional Gated Recurrent Unit …

45

Table 4 Performance of BGRU at different number of hidden units Metric TPR

FPR

MCC

Units

DDoS

DoS

Normal

10

95.41

97.98

0.00

Reconn. 99.78

Theft

Mean ± Stdev

0.00

58.64 ± 53.55

50

97.84

98.30

61.18

99.83

0.00

71.43 ± 43.11

100

97.91

98.57

69.41

99.98

75.00

88.17 ± 14.73

150

98.01

98.86

74.12

99.99

75.00

89.20 ± 13.38

200

97.97

99.18

76.47

99.98

75.00

89.72 ± 12.80

10

1.92

4.40

0.00

0.01

0.00

1.27 ± 1.94

50

1.62

2.07

0.00

0.00

0.00

0.74 ± 1.02

100

1.36

2.00

0.00

0.00

0.00

0.67 ± 0.95

150

1.08

1.90

0.00

0.00

0.00

0.60 ± 0.87

200

0.78

1.94

0.00

0.00

0.00

0.55 ± 0.85

10

97.43

96.42

0.00

99.94

0.00

58.76 ± 53.65

50

98.28

98.12

89.11

99.96

0.00

77.09 ± 43.30

100

98.49

98.23

91.66

99.99

93.30

96.33 ± 3.63

150

98.72

98.38

93.05

99.99

93.30

96.69 ± 3.27

200

98.92

98.43

93.72

99.99

93.30

96.87 ± 3.12

better than BGRU-HU1, BGRU-HU2, BGRU-HU3, and BGRU-HU4. BGRU-HU5 increased TPR by 53, 25.61, 1.75, and 0.58% relative to BGRU-HU1, BGRU-HU2, BGRU-HU3, and BGRU-HU4, respectively; FPR decreased by 56.69%, 25.68%, 17.91%, and 8.33%, respectively; and MCC increased by 64.86, 25.66, 0.56, and 0.19%. Therefore, a sufficiently large number of hidden units in BGRU will ensure a high detection rate and reduce false alarm in the IoT botnet detection system developed for smart homes.

3.5 Influence of Batch Size on Classification Performance In this subsection, we determine the optimal batch size required for efficient BGRUbased IoT botnet detection in smart homes. Five single layers BGRU neural networks with batch sizes of 32, 64, 128, 256, and 512 formed BGRU-B32, BGRU-B64, BGRU-B128, BGRU-B256, and BGRU-B512 classifiers, respectively, when trained using 200 hidden units, ReLU activation function, five epochs, and Adam optimizer. Training and validation losses in BGRU-B32, BGRU-B64, BGRU-B128, BGRUB256, and BGRU-B512 were analyzed to understand the extent of model underfitting and overfitting, respectively. Figures 9 and 10 show that the lowest training and validation losses were realized with a batch size of 32. In general, training and validation losses reduced in all the classifiers throughout the five-epoch period. However, training and validation losses were lower in BGRU-B32 than in BGRU-B64, BGRUB128, BGRU-B256, and BGRU-B512. At the end of the experiment, we observed

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Batch Size (32) Batch Size (64) Batch Size (128) Batch Size (256) Batch Size (512)

0.35

Training Loss

0.3 0.25 0.2 0.15 0.1 0.05 0 1

2

3

4

5

Epoch Fig. 9 Training loss of different batch sizes 0.22

Batch Size (32) Batch Size (64) Batch Size (128) Batch Size (256) Batch Size (512)

0.2 0.18

Validation Loss

0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 1

2

3

Epoch Fig. 10 Validation loss of different batch sizes

4

5

Deep Bidirectional Gated Recurrent Unit …

47

that training loss in BGRU-B32 reduced to 0.0287, while validation loss reduced to 0.0262. BGRU-B32 reduced the average training losses in BGRU-B64, BGRUB128, BGRU-B256, and BGRU-B512 by 18.49%, 35.38%, 49.58%, and 62.48%, respectively, while the average validation losses were reduced by 20.52%, 38.3%, 52.24%, and 64.55%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is best minimized when the batch size in BGRU was 32. In other words, a sufficiently small batch size in BGRU will facilitate high classification accuracy and good generalization ability that are required for efficient IoT botnet detection in smart homes. Multi-class classification performance of BGRU-B32, BGRU-B64, BGRU-B128, BGRU-B256, and BGRU-B512 was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. Table 5 shows that BGRU-B32 performed better than BGRU-B64, BGRU-B128, BGRU-B256, and BGRU-B512. BGRU-B32 increased TPR by 0.42%, 0.86%, 12.80%, and 26.49% relative to BGRU-B64, BGRU-B128, BGRU-B256, and BGRU-B512, respectively; FPR decreased by 35%, 52.73%, 60%, and 69.05%, respectively; and MCC increased by 0.21%, 0.42%, 3.84%, and 26.24%. Therefore, a sufficiently small batch size in BGRU will ensure a high detection rate and reduce false alarm in the botnet detection system developed for smart homes. Figure 11 shows that training time decreased as the batch size increased. BGRUB32 took the longest time (101.35 min) to train, while the shortest training time of 6.70 min was achieved in BGRU-B512. Table 5 Performance of BGRU at different batch sizes Metric TPR

FPR

MCC

Units

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev

32

99.12

99.51

78.82

99.99

75.00

90.49 ± 12.47

64

98.60

99.29

77.65

99.99

75.00

90.11 ± 12.63

128

97.97

99.18

76.47

99.98

75.00

89.72 ± 12.80

256

97.70

98.93

67.06

99.90

37.50

80.22 ± 27.57

512

97.60

98.01

62.35

99.76

0.00

71.54 ± 42.95

32

0.47

0.85

0.00

0.00

0.00

0.26 ± 0.38

64

0.68

1.34

0.00

0.00

0.00

0.40 ± 0.60

128

0.78

1.94

0.00

0.00

0.00

0.55 ± 0.85

256

1.02

2.21

0.00

0.00

0.00

0.65 ± 0.98

512

1.89

2.31

0.00

0.00

0.00

0.84 ± 1.16

32

99.44

99.28

94.39

100.00

93.30

97.28 ± 3.18

64

99.16

98.88

94.06

100.00

93.30

97.08 ± 3.14

128

98.92

98.43

93.72

99.99

93.30

96.87 ± 3.12

256

98.68

98.18

90.94

99.97

80.62

93.68 ± 8.11

512

98.02

97.88

89.48

99.94

0.00

77.06 ± 43.27

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Fig. 11 Training time of different batch sizes

3.6 Influence of Optimizers on Classification Performance In this subsection, we determine the most suitable optimizer required for efficient BGRU-based IoT botnet detection in smart homes. Four single layers BGRU neural networks with Adam, SGD, RMSprop, and Adadelta optimizers formed BGRU-OP1, BGRU-OP2, BGRU-OP3, and BGRU-OP4 classifiers, respectively, when trained using ReLU activation function, five epochs, and a batch size of 128. Training and validation losses in BGRU-OP1, BGRU-OP2, BGRU-OP3, and BGRU-OP4 were analyzed to understand the extent of model underfitting and overfitting, respectively. Figures 12 and 13 show that the lowest training and validation losses were realized with Adam optimizer. In general, training and validation losses reduced in all the classifiers throughout the five-epoch period. However, training and validation losses were lower in BGRU-OP1 than in BGRU-OP2, BGRU-OP3, and BGRU-OP4. At the end of the experiment, we observed that training loss in BGRU-OP1 reduced to 0.0486, while validation loss reduced to 0.0458. BGRU-OP1 reduced the average training losses in BGRU-OP2, BGRU-OP3, and BGRU-OP4 by 86.54%, 4.22%, and 90.40%, respectively, while the average validation losses were reduced by 89.70%, 5.49%, and 92.31%, respectively. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is best minimized when Adam optimizer was employed in BGRU. In other words, the use of Adam optimizer in BGRU will facilitate high classification accuracy and good generalization ability that are required for efficient botnet detection in smart homes.

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1.7 Adam

1.6

SGD

1.5

RMSprop Adadelta

1.4 1.3

Training Loss

1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1

2

3

4

5

Epoch Fig. 12 Training loss of different optimizers 1.4 Adam

1.3

SGD

1.2

RMSprop Adadelta

1.1

Validation Loss

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1

2

3

Epoch Fig. 13 Validation loss of different optimizers

4

5

50

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Table 6 Performance of BGRU for different optimizers Metric

Optimizer

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev

TPR

Adam

97.97

99.18

76.47

99.98

75.00

89.72 ± 12.80

FPR

SGD

82.42

66.80

0.00

0.00

0.00

29.84 ± 41.24

RMSprop

98.35

98.21

22.35

99.82

0.00

63.75 ± 48.64

Adadelta

99.89

3.63

0.00

0.00

0.00

20.70 ± 44.29

Adam

0.78

1.94

0.00

0.00

0.00

0.55 ± 0.85

SGD

35.06

18.19

0.00

0.00

0.00

10.65 ± 15.75

1.70

1.59

0.00

0.01

0.00

0.66 ± 0.90

RMSprop MCC

Adadelta

92.35

3.74

0.00

0.00

0.00

19.22 ± 40.92

Adam

98.92

98.43

93.72

99.99

93.30

96.87 ± 3.12

SGD

72.17

76.90

0.00

0.00

0.00

29.81 ± 40.86

RMSprop

98.35

98.43

73.64

99.95

0.00

74.07 ± 42.83

Adadelta

52.78

49.79

0.00

0.00

0.00

20.51 ± 28.11

Multi-class classification performance of BGRU-OP1, BGRU-OP2, BGRU-OP3, and BGRU-OP4 was evaluated with respect to the ground truth labels based on TPR, FPR, and MCC. Table 6 shows that BGRU-OP1 performed better than BGRU-OP2, BGRU-OP3, and BGRU-OP4. BGRU-OP1 increased TPR by 200.67%, 40.74%, and 333.43% relative to BGRU-OP2, BGRU-OP3, and BGRU-OP4, respectively; FPR decreased by 94.84%, 16.67%, and 97.14%, respectively; and MCC increased by 224.96%, 30.78%, and 372.31%. Therefore, the adoption of Adam optimizer in BGRU will ensure a high detection rate and reduce false alarm in the botnet detection system developed for smart homes.

3.7 Performance of Deep BGRU-Based Multi-class Classifier In this subsection, we evaluate the suitability of deep BGRU for IoT botnet detection in smart homes. A deep BGRU multi-class classifier was developed with the optimal hyperparameters in Sects. 3.1–3.6, namely, ReLU activation function, 20 epochs, 4 hidden layers, 200 hidden neurons, a batch size of 512, and Adam optimizer. Training and validation losses in deep BGRU multi-class classifiers were analyzed to understand the extent of model underfitting and overfitting, respectively. Figure 14 shows that the training and validation losses were shallow when the optimal BGRU hyperparameters were used. Training and validation losses reduced throughout the five-epoch period. At the end of the experiment, we observed that training loss in deep BGRU multi-class classifiers reduced to 0.0018, while validation loss reduced to 0.0006. The reduction in training and validation losses implies that the likelihood of model underfitting and overfitting is minimized when the optimal hyperparameters were employed in BGRU. In other words, the choice of the optimal BGRU

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51

0.11 0.1 Training loss Validation loss

0.09 0.08

Loss

0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 0

2

4

6

8

10

12

14

16

18

20

Epoch Fig. 14 Training and validation losses of deep BGRU classifier

hyperparameters will facilitate high classification accuracy and good generalization ability that are required for efficient IoT botnet detection in smart homes. The time needed to train the optimal deep BGRU classifier was 33.95 min. Multi-class classification performance of deep BGRU multi-class classifier was compared with the state-of-the-art methods based on TPR, FPR, and MCC. Tables 7, 8, and 9 show that deep BGRU multi-class classifier outperforms mixture localization-based outliers (MLO) [47], SVM [48], RF [49], artificial immune system (AIS) [50], and feedforward neural network (FFNN) [51]. Deep BGRU multi-class classifier achieved high detection accuracy and low false alarm with true positive rate (TPR), false positive rate (FPR), and Matthews coefficient correlation (MCC) of 99.28 ± 1.57%, 0.00 ± 0.00%, and 99.82 ± 0.40%. Table 7 TPR of multi-class classifiers for IoT botnet detection in smart homes Ref MLO [47]

DDoS

Reconn.

Theft

Mean ± Stdev

DoS

Normal

98.24

98.20

0.00

95.67

95.86

77.59 ± 43.39 97.22 ± 5.19

SVM [48]

98.95

99.87

87.97

99.33

100.00

RF [49]

99.40

0.00

0.00

99.37

99.38

59.63 ± 54.43

AIS [50]

100.00

98.53

0.00

98.22

98.90

79.13 ± 44.24

FFNN [51] Deep BGRU

99.414 100.00

0.00

98.071

98.38

88.92

76.96 ± 43.23

99.99

96.47

99.96

100.00

99.28 ± 1.57

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Table 8 FPR of multi-class classifiers for IoT botnet detection in smart homes Ref

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev

SVM [48]

0.00

0.26

0.00

5.96

0.01

1.56 ± 2.36

Deep BGRU

0.01

0.00

0.00

0.00

0.00

0.00 ± 0.00

Table 9 MCC of multi-class classifiers for IoT botnet detection in smart homes Ref

DDoS

DoS

Normal

Reconn.

Theft

Mean ± Stdev

SVM [48]

99.74

99.81

96.90

96.82

100.00

98.65 ± 1.64

Deep BGRU

99.99

99.99

99.11

99.99

100.00

99.82 ± 0.40

4 Conclusion In this paper, an optimal model was developed for efficient botnet detection in IoTenabled smart homes using deep BGRU. A methodology was proposed to determine the optimal BGRU hyperparameters (activation function, epoch, hidden layer, hidden unit, batch size, and optimizer) for multi-class classification. The proposed methodology was implemented, and the classification performance was jointly assessed based on training loss, validation loss, accuracy, TPR, FPR, MCC, and training time. Extensive simulation results showed that: (a) ReLU performed better than tanh activation functions; (b) classification performance improved with an increase in the numbers of epochs, hidden layers, and hidden units; (c) the performance of BGRU improved as the batch size becomes smaller, but this comes with a significant increase in training time; (d) Adam outperformed SGD, RMSprop, and Adadelta optimizers. Finally, the combination of ReLU activation function, 20 epochs, 4 hidden layers, 20 hidden units, a batch size of 512, and Adam optimizer achieved the best multi-class classification performance as follows: low training loss (0.0107 ± 0.0219), validation loss (0.0072 ± 0.0086), FPR (0.00 ± 0.00%); high accuracy (99.99%), TPR (99.28 ± 1.57), and MCC (99.82 ± 0.40). Acknowledgments The authors would like to thank Cyraatek UK and the Faculty of Science & Engineering, Manchester Metropolitan University, for the research grant provided.

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Big Data Clustering Techniques: Recent Advances and Survey Ibrahim Hayatu Hassan, Mohammed Abdullahi, and Barroon Isma’eel Ahmad

Abstract Clustering as an unsupervised machine learning technique has appeared as a great learning method to examine correctly the huge volume of dataset produced by today’s applications. There is a huge amount of information in the field of clustering and a lot of attempts have already been made to identify and evaluate it for a wide number of applications, but the major problems with the application of classical clustering algorithm for big data analysis are its high complicity, massive volume, variety, and generation rate. So, the classical clustering methods are becoming increasingly inept in processing such data. This poses exciting challenges for researchers to develop modern scalable and efficient methods of clustering that can extract useful information from these vast amounts of data produced in different areas of life. Here in this paper we present a classification for the review of big data clustering algorithms by identifying major research subjects. We then present an up-to-date review of the study works within each of the subject. Keywords Big data · Clustering · MapReduce · Spark · MPI · GPU

1 Introduction Clustering is an unsupervised machine learning method used to divide unlabeled dataset into groups called clusters, which comprise data elements that are dissimilar from those in other groups and similar to each other in the same cluster [1]. This has been considered as an important technique for extracting knowledge from databases and has been widely applied to a variety of scientific and business areas [2]. In the H. Ibrahim Hayatu Institute for Agricultural Research, Ahmadu Bello University, Zaria, Nigeria e-mail: [email protected] H. Ibrahim Hayatu · A. Mohammed (B) · A. Barroon Isma’eel Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria e-mail: [email protected] A. Barroon Isma’eel e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_3

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present digital age, based on the enormous advancement and growth of the internet and web technologies, the data produced by machines and devices has reached a huge volume and is expected to increase in the coming years [3]. Data has become the most integral part of the knowledge-driven society and the economy of today. The goal of big data clustering is to summarize, segment, and group the enormous volumes and data diversities generated in groups of similar items at an accelerated rate. In exploratory data analysis, this has turned out to be one of the most important methods. Sadly, the classical clustering methods are becoming increasingly ineffective in the processing of these data due to their high complicity, large volume, variety, and generation rate. This poses exciting opportunities for researchers to develop scalable and efficient methods of clustering that can extract useful information from these vast amounts of data produced in different areas of life [4, 5]. Therefore, the proposed paper presents an up-to-date review of the research works presented to handle big data clustering. The remainder of this paper is structured as follows: Sect. 2 includes a summary of the different approaches to clustering. Section 3 provides an overview of different big data clustering approaches. Section 4 describes numerous applications in the real world that used specific types of the big data clustering. Finally, the paper is summarized in Sect. 5.

2 Clustering Techniques A number of clustering algorithms have been proposed in the literature. These algorithms are typically grouped according to the basic principle that the clustering algorithm is based on [6]. That leads to the following groups being identified as shown in Fig. 1.

Fig. 1 Grouping of clustering approaches

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2.1 Partitioning Methods Clustering partitioning algorithms organize items into a given number of groups by maximizing a specific objection function capturing the grouping structure. Most partitioning algorithms start via an initial assignment and then use an iterative method which transfers items from cluster to cluster to maximize the objective function. Convergence is therefore local and the best global solution that cannot be guaranteed. These methods required that the number of clusters should be known at the initial stage, and this is normally set by the user [6]. Most of the research in clustering comes from the family of partitioning algorithms. K-means [7], k-mode [8], k-prototype [9], and fuzzy c-means [10] are the examples of the partitioning methods.

2.2 Hierarchical Clustering Methods Hierarchical clustering techniques build a clusters hierarchy in a top-down approach (Divisive) or using bottom-up approach (Agglomerative) [11]. All these approaches rely on building a similarity matrix between all of the data items; the choice of the metric for the construction of the matrix may affect the shape of the clusters. Examples of common similarity metric that can be employed are: cosine and Jaccard distance metric. Linkage criteria that calculate the distance between groups of objects is a function of the similar distance between objects. But the problem with hierarchical clustering algorithm is that they have large time complexity “O(n 3 )” and needs “O(n 2 )” space, for which n represents the number of data items. It can be never undone once the merge or the split step is completed. CURE [12], ROCK [13], and BIRCH [14] are some of the well-known hierarchical clustering algorithms.

2.3 Density-Based Methods Data items are clustered in density-based method on the basis of their boundary, connectivity, and regions of density. A cluster described as a dense component that is connected grows in any direction to which density leads. Such methods are related to neighbours closest to the point. Density-based algorithms are capable of identifying clusters of arbitrary types and providing natural immunity to outliers [15]. Therefore, the total density of a particular point is examined to evaluate the features of the dataset that influence a particular data point. DBSCAN [16], OPTICS [17], DBCLASD [18], and DENCLUE [19] are the most widely used density-based clustering algorithms.

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2.4 Grid-Based Algorithms The data item space in grid-based technique is split into grids. The key benefit for this method was its great ability to process, and this is because it runs once over the dataset to measure the statistical grid values. Grid-based clustering is performed on the grid, instead of clustering directly from the database. Quality is heavily dependent on the grid size, which is usually smaller than that of the database. However, the use of a single uniform grid might be insufficient for highly irregular data distributions to find a reliable clustering performance or to meet the time limitation. STING [20], CLIQUE [21], OptiGrid [22], and Wave-Cluster [23] are the common examples of this approach.

2.5 Model-Based Methods Model-based techniques attempt to adjust the fit between some predetermined mathematical formula and data based on the idea that data is created by a combination of simple distributions of probability. This results in the automated detection of number of clusters based on standard statistics, bringing outliers into perception, and thereby building a reliable clustering strategy. Statistical and neural networks are the two major approaches on the model-based techniques. MCLUST by Fraley and Raftery [24] is regarded as the most common model-based algorithm but there are other algorithms like COBWEB [25] (conceptual clustering), EM [26], and neural network methods like self-organizing feature maps [27]. Neural network system uses a combination of linked input/output units with each connection’s weight associated. Neural network uses several attributes for clustering which made them famous. The statistical methods employ probability procedures when evaluating clusters. Probabilistic definitions are usually used to describe any idea derived from them [15].

3 Big Data Clustering Approaches Based on the combination of conventional clustering and acceleration techniques, many approaches have been proposed in the literature to manage huge data. This approach focuses on increasing the speed of the clustering process by decreasing the complicacy of the computation. On the basis of the acceleration method that is used to enhance the scalability, big data clustering methods can be classified into parallel techniques, data reduction-based techniques, and centre reduction-based techniques as shown in Fig. 2.

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Fig. 2 An overview of big data clustering

3.1 Data Reduction-Based Methods This technique attempts to reduce the number of data items during cluster development in order to hasten the clustering technique. The MinBatch k-means method (MBKM) was introduced by Sculley [28]. Its basic idea is the use of random small split of fixed size data items which can be placed in memory. This technique is based on the fact that random lots appear to be noisier than single data points. Alternative to k-means for massive-scale data processing named recursive partition k-means (RPKM) has been introduced by Capó et al. [29]. The idea of this technique is to estimate the k-means for the entire dataset by attempting the iterative application of a weighted version of k-means over a small number of data subsets. RPKM’s key steps are defined as follows: first, the data are divided into a number of sets of data in which each set is defined by a member and their corresponding weight. Secondly, over the collection of members, weighted variant of k-means is used.

3.2 Centre-Based Reduction Methods This technique tries to decrease the amount of evaluations when searching for the nearby cluster centres, which are the most time-consuming step. Kanungo et al. [30] proposed the use of kd-tree structure to speed up k-means (KdtKM). kd-Tree is defined as a binary tree which divides the data space constructed by piercing hyperplanes. The k in kd-tree signify data dimension. Chen et al. [31] proposed a fast density peak clustering for large-scale data based on kNN. Density peak (DPeak) clustering algorithm is not valid for large-scale data, due to two quantities, i.e., both of which are acquired with complexity O(n2) by brute force algorithm. Hence, a simple but fast DPeak was proposed, namely FastDPeak, which runs in projected time in the intrinsic dimensionality around O(nlog(n)). This replaces density with kNNdensity, which is determined by fast kNN algorithms such as cover tree, resulting in enormous density calculation improvements. Based on kNN-density, local density peaks and non-local density peaks are defined, and a fast algorithm is also proposed with complexity O(n), which uses two different strategies to calculate for them. Experimental tests show FastDPeak to be successful and to outperform other DPeak variants.

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3.3 Parallel Techniques Parallelization technique is among the most frequently used method to reduce the time complexity of traditional clustering techniques. Parallelization is described as a method that splits the task into chunks and executes them in parallel. In the literature, many parallel clustering techniques were proposed. Various frameworks like graphics processing unit [32], message passing interface [33], MapReduce [34], or Spark [35] may be used for parallelization. In this part, we review various research works on parallel clustering techniques with focus on such frameworks. Graphical Processing Unit-Based Techniques (GPU). GPU is a customized technology built to speed up graphical activities like photo and video formatting. A parallel programming paradigm called compute unified device architecture (CUDA) was used to shorten the development implementations of parallel GPU without going into the details of the technology. Particularly in comparison to a central processing unit (CPU), GPU consists of a huge number of processing cores. It also offers two parallelization stages. GPU will have many multiprocessors on the initial stage; then at the next stage every multiprocessor will also have several streaming processors. Having followed this layout, the GPU application is split into chunks running on streaming processors, and then these chunks are gathered to form chunks blocks running on a multiprocessor [32]. Compared with the few terabytes of RAM now allowed in servers, the comparatively small amount of video random access memory (VRAM) on a GPU card has prompted some to assume that GPU acceleration is restricted to applications with “small data.” But, the assumption lacks two common practices in applications of “big data.” One is that processing a whole dataset at once is rarely needed to achieve the desired effect. GPU VRAM, system RAM, and storage [direct attached storage (DAS), storage area networks (SAN), networkattached storage (NAS), etc.] can provide virtually unlimited large data workload volume. For machine learning, for instance, the training data can be streamed from memory or storage as required. Live streams of data coming from the internet of things (IoT) or other applications such as the Kafka or Spark may also be consumed in a common “incremental” method. The second technique is being able to scale up and out GPU-accelerated configurations. Numerous GPU cards can be cited in a single server, and several servers can be organized in a cluster. This scaling resulted in more cores and more memory, all functioning concurrently and enormously in parallel to process data at unparalleled speed. Therefore, the only real limit to GPU acceleration’s potential computing power is the budget. However, whatever the budget available, it will still be possible for a GPU to speed up configuration and produce more flops per dollar because CPUs are and will remain much more costly than GPU. The GPU database offers a simple and potentially significant price/performance advantage in a single server or cluster [36]. To quicken data clustering via GPU, several works have been proposed, such as the work of Che et al. [37] that introduced a k-means algorithm based on GPU (GPUKM). First, the initial centroid is posted into the GPU’s shared memory and the input data is split and send to each of the multiprocessor. And from there, the distance from each corresponding data item is computed

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by each multiprocessor and allocated to the nearest cluster. Depending on the subset of the data items, a local cluster centroid is computed. The central processing unit (CPU) updates the new cluster centroids after each data items is allocated to cluster centroids and submits them to the multiprocessors once more. The above steps will be called multiple times using the same technique until convergence is achieved. The parallel k-means reduces the time complexity of the algorithm. Andrade et al. [38] proposed a GPU-enhanced algorithm for density-based clustering (G-DBSCAN). This is split into two phases: the first phase involves building graph. Each objects represent a node and an edge is created between two objects if the distance is lower than or equal to a predefined thresholds. Once the graph is set, the second phase is the identification of clusters that use breath first search algorithm (BFS) to traverse the graph produced from the first phase. Both phases of the algorithm have been parallelized to achieve better performance. Result indicates that in comparison to the serial implementation, G-DBSCAN is 112 times faster. A GPU fuzzy c-means technique (GPUFCM) was proposed by Al-Ayyoub et al. [39]. The starting cluster locations are first stored in the shared memory. It then produces from the dataset the starting participation matrix and the starting cluster centroids. After that, by calculating distances, each multiprocessor calculates partial participation. It then calculates the participation values by adding partial participation. The added participation values are transferred from the GPU to the CPU to calculate new cluster centroids from which the algorithm will move to the next iteration. Cuomo et al. [40] suggest an enhanced GPU-based k-means algorithm. The proposed work adopted a parallel processing structure of the graphical processing unit (GPU). The design proposed was optimized to handle CPU space inefficiency and data transmission time for the host device. A parallelize kernel k-means using CPU and GPU was proposed by Baydoun et al. [41]. Kernel k-means involves several computational phases and has additional computational criteria. As a consequence, kernel k-means has not seen the same value and much can still be achieved with respect to its parallelization and stable implementations. Several databases are used, with varying number of features and patterns. The findings show that CUDA typically has the best runtimes with speed-ups ranging from two to more than 200 times over a single-core CPU implementation depending on the dataset used. Shahrezaei and Tavoli [42] studied parallelization of k-means ++ with CUDA. k-means ++ is an algorithm developed to boost the method of locating initial seeds in the algorithm k-means. In this algorithm, the initial seeds are selected consecutively through a probability proportional to the distance from the nearest node. The most critical problem of this algorithm is that it reduces the speed of clustering while running in serial way. This work parallelized the k-means ++ algorithm’s most time-consuming steps to increase the runtime of the k-means algorithm while using the k-means ++ seeding technique. In the k-means ++ algorithm, the distance of all points to the selected centre should be determined and the seeds are selected according to a probability formula. Those are the most difficult things for the k-means ++ algorithm being parallelized. This work’s parallelization process starts with the partitioning of the points into l different points. Then the measurement of the distance of points to the centres remains parallel until the end of the algorithm. The results of this work

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show that the algorithm performs more efficiently over GPU than CPU. It has also been shown that constant memory and texture memory have greater effectiveness in this research than global memory. Even with GPU’s certified success in managing massive-scale dataset, it tends to suffer from memory threshold. For instance, it may not handle terabyte data with a bound of 12 GB of memory for every GPU. The efficiency of the GPU-based technique goes way down once the size of the data surpasses the GPU memory capacity. Therefore, developers should first configure the memory before implementing programs via GPU. The drawback to this is the small number to usable GPU tools and algorithms. Message Passing Interface Techniques. MPI is a language-independent parallel computing communication protocol that supports point-to-point and collective communication [43]. High performance, scalability, and portability are MPI targets. MPI is typically the prevalent model used in high-performance computing [44] and provides portability among parallel programs running on distributed memory systems. But the standard presently does not really support fault tolerance [44] because it primarily addresses problems with high performance computing (HPC). Another MPI downside is that leveraging the parallelism of multicore architectures for shared memory multiprocessing, for example, is not ideal for small grain degree of parallelism. Most researchers seek to combine the MPI with other application programming interface (API), such as the OpenMP, to overcome this issue. OpenMP is an API that enables multiplatform multiprocessing programming for shared memory [45] on most processor architecture and operating systems. OpenMP for its high performance is becoming the standard for parallel shared memory computing but it is not suitable for distributed memory system. The combination of these methods therefore allows for two levels of granularity: a small grain parallel with OpenMP and a large grain parallel with MPI. MPI is implemented in the design of master/slave whereby the master transmits tasks to slaves and collects computed outcomes. However, the job of the slave is to accept tasks, process, and then send the output to the master. Many techniques of clustering that use the MPI paradigm were proposed. For example, Kwok et al. [46] proposed a parallel fuzzy c-means algorithm based on the MPI paradigm (MPIFCM). The master first divides the input data into chunks and sends it to slaves. Each slave gets the associated chunk; it updates the membership matrix based on the distance measurement. The master then receives all the information needed for the slaves to measure the new cluster. These steps are repeated until convergence. A comparative study was made between the MPIFCM and the parallel k-means, which shows a similar structure of parallelism between the two algorithms. A particular implementation of the proposed algorithm is used and tested to cluster a large dataset. It is shown that the MPIFCM algorithm has nearly ideal speed-ups for large dataset and performs equally well when demanding more clusters. Experimentally, the scale-up performance regarding the size of the dataset is also shown to be excellent. Zhang et al. [47] developed a k-means (MPIKM) based on MPI. In this technique, the input data is shared among the slaves. After that, the master chooses k data items as the starting centroid and sends them to the slaves. Each one of the slaves then determines the distance between each data object and

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the cluster centroids and assigns it to their nearest cluster. The master then receives all the necessary information from the slaves. The entire process is reiterated until convergence. The experimental results based on the seven datasets used have shown that by increasing the dataset the running time of the proposed MPIKM algorithm is slightly lower than that of the standard k-means. The result also shows that for a small-scale dataset, it is not a good choice to use the proposed algorithm because the time of dividing the dataset and assigning the task into each process occupies a certain proportion. Savvas and Sofianidou [48] proposed a new semi-parallel variant of the k-means algorithm for n-dimensional data objects via MPI. The first step of the proposed strategy is for the master node to discover the resources available and to divide the data into equal or nearly equal parts. The master node passes the data subsets to the workers who in turn start applying the original sequential k-means algorithm on it after obtaining the data. The worker nodes move the local centroids and the number of data points allocated to each centroid to the master node after its termination, and their job ends here. The master node calculates the global centroids which apply the weighted arithmetic mean after collecting this information. The master node at first sorts out the centroids and then divides them into k subgroups and determine the global centroids (by the use of weighted arithmetic mean). The centroids generated from the sequential algorithm by the proposed technique use one-dimensional data points. On increasing the size of n-dimensional data points, the technique generates centroids very similar to the original algorithm with similarities varying from 91 to 100%. Shan et al. [49] proposed a new parallel k-means algorithm for high-dimensional text data using both GPU and MPI. The GPU part design of this work focuses on the measure of similarity between text objects, that is, the computation of the matrix. The original complexity of computation is O(nd). In parallel design, it is designed to start n threads, which runs in parallel. Complexity becomes O(d) which greatly reduces runtime of the algorithm. The portion of the MPI divides nodes into one control node and multiple compute nodes. In order to test the runtime of the proposed technique, the algorithm was tested on CPU, GPU, and GPU + MPI platforms. The results show that the use of MPI and GPU can not only increase the performance of the algorithm, but can also be easily potted to the GPU cluster. However, even with the effectiveness of the MPI paradigm for massive-scale data handling, it is affected by fault intolerance limit. MPI seems to have no fault management tool. A machine downfall in the system can make the entire system to be closed down. In MPI-based techniques developers, therefore, must introduce a fault tolerance method in the design of master/slave to handle machine failures. MapReduce-Based Techniques. MapReduce has recently become one of the most widely used parallel programming paradigms for handling massive-scale data along clusters systems. It is defined for its strong openness, which enables designers to easily and accurately parallelize algorithms. The MapReduce paradigm consists of two parts, which are Map and Reduce. The map component runs the map functions to create a set of intermediate pairs that process each in parallel. All intermediate values connected to the same intermediate key are grouped together, while the reduce part executes the reduce function to combine

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all intermediate values linked to the same intermediate key. The key factors that make MapReduce a good paradigm for handling massive data are the fault tolerance, linear scalability, and simple programming paradigm [34]. A large number of research works have suggested in the literature that clustering method is suitable via MapReduce. For instance, Zhao et al. [50] implemented k-means based on MapReduce (MRKM) in Hadoop framework. This approach divides the input dataset into blocks, where each block is sent to the mapper and each data element is assigned to its closest cluster based on the distance computation by the map function. By computing the mean data item of each cluster, the reduce function updates the new cluster centroids at the reduce phase. Then the new cluster centroids are taken to the Hadoop distributed file system (HDFS) for the next iteration which is to be used by the map function. In conclusion, until convergence, this whole process will continue. This way the time complexity of the k-means algorithm is reduced. Kim et al. [51] proposed an effective clustering algorithm based on density for massive data using MapReduce (DBCURE-MR). Although the conventional density-based methods discover every cluster one after the other, the proposed algorithm discovers multiple clusters together in parallel. The DBCURE parallelization using MapReduce was performed in four stages. First, the matrix of neighbourhood covariances was calculated in parallel for every point. Similarity joins were performed in the second stage to explore all pairs of points, each of which is inside each of their neighbourhood. In the third stage, core clusters were identified. The core clusters were continuously fusing through the last stage to create the final clusters. Experimental results indicate that DBCURE-MR proficiently discovers specific groups and scales well with MapReduce paradigm. Ludwig [52] proposed a MapReduce fuzzy c-means clustering (MRFCM). Two MapReduce jobs were used in this work. First MapReduce job computes the cluster centre matrix and the second MapReduce calculates the distances that will be used to update the matrix. Using this method the time complexity of the fuzzy c-means was reduced in comparison with the serial fuzzy c-means algorithm. Another MapReduce-based k-prototype (MRKP) for clustering massive-scale mixed data was also proposed by Ben HajKacem et al. [53]. Shahrivari and Jalili [54] implemented a single-pass model based on MapReduce termed as (MRK-means) using the re-clustering strategy. Unlike other MapReducebased k-means implementations, MRK-means needs only to read the dataset once and is therefore multiple times faster than the original k-means. MRK-means time complicacy is linear which is smaller than that of iterative k-means. Because of the use of the seeding method, MRK-means also lead to clusters of greater quality. Dongbo et al. [55] also proposed a canopy k-means (CK-means) clustering algorithm based on MapReduce. Two MapReduce are being used in this work. The first MapReduce estimates the number of clusters and centroids to be used by the second MapReduce using a pre-clustering algorithm. The second MapReduce performed the clustering of k-means by dividing the dataset in which each partition is associated with a map function. The map function assigns every data point of the associated segment to the nearest cluster by measuring the distance between the objects and the cluster centres. In the reduction function the cluster centres are then updated by computing the cumulative average of all data points in each cluster. The new centres are then moved to

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the HDFS, which the map function can use for the next iteration. The whole process is repeated until convergence, which reduces the time complexity of the algorithm compared to the original k-means algorithm. Nonetheless, the algorithm’s immunity to noise is low and the threshold values of the canopy algorithm are very difficult to determine, which has some effect on the result. In the work of Valcarce et al. [56], an implementation of posterior likelihood clustering and recommendation-relevance models based on MapReduce was proposed. In the current environment where the number of recommendations hitting the big data scale is rising day after day, high efficiency estimates are not sufficient. This research addresses one urgent and important critical need for recommendation systems, which is scalability of algorithms. The research adapted these highly effective algorithms to the functional MapReduce paradigm, which was earlier proved as an appropriate tool to allow scalability of the recommender. A good scalability behaviour was achieved with respect to the number of nodes in the MapReduce cluster. To evaluate large datasets, the impulse for today’s scenario is to boost the conventional methods. Tripathi et al. [57] suggest an efficient clustering tool called an enhanced grey wolf optimizer (MR-EGWO) based on MapReduce for clustering large-scale datasets. The method implemented a new type of grey wolf optimizer, called enhanced grey wolf optimizer (EGWO), in which grey wolf’s hunting strategy is hybridized with binomial crossover and levy flight steps are induced to boost the searching capability for pray. The suggested variant is further used to improve the clustering method. The EGWO’s clustering performance is evaluated on seven UCI benchmark datasets and compared to the five current clustering techniques, namely k-means, particle swarm optimization (PSO), gravitational search algorithm (GSA), bat algorithm (BA), and grey wolf optimizer (GWO). The EGWO’s convergence behaviour and consistency were validated through the convergence graph and boxplots. Therefore, in the Hadoop context the proposed EGWO is parallelized on the MapReduce model and called MR-EGWO to manage the large-scale datasets. Furthermore, the MR-EGWO’s clustering accuracy is also validated in terms of F-measure and compared to four state-of-the-art MapReducebased algorithms, namely, parallel k-means, parallel K-PSO, MapReduce-based artificial bee colony optimization (MR-ABC), and dynamic frequency-based parallel k-bat algorithm (DFBPKBA). Experimental findings confirm that the proposed technique is promising and effective substitute for the efficient and large-scale data clustering. Classical clustering methods generally represent each cluster in all dimensions without any difference and depend only on an individual dimension of projection as the weight of an attribute ignores relevance among attributes. Pang et al. [58] address these two problems with the use of multiattribute weights using a MapReduce-based subspace clustering algorithm called PUMA. The subspaces attribute is developed into the PUMA by calculating a value weight attribute dependent on the probability of co-occurrence of attribute values between different dimensions. PUMA obtains parallel sub-clusters of each computing node corresponding to the respective attribute subspaces. Finally, by applying the hierarchical clustering method, PUMA tests different scale clusters to iteratively combine sub-clusters. PUMA was deployed on an Hadoop cluster of 24-nodes. Findings show that using multiattribute weights with subspace clustering can achieve better clustering accuracy in both the artificial

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and the real-world large datasets. The results also show that in terms of number of nodes PUMA achieves great success in terms of extensibility, scalability, and the almost linear speed-up. In addition, experimental findings show that PUMA is realistic, efficient, and functional for expert systems such as automatic abstracting, acquiring information, recommending systems, and disambiguating word sense. While MapReduce tends to be ideal for clustering large-scale data, it tends to suffer from the inefficiency of performing iterative operations [59]. The whole data must be read and written back to the HDFS at each iteration of the technique. Consequently, multiple I/O memory operations occur during every iteration, and this significantly reduces the effectiveness of MapReduce-based technique. Spark-Based Techniques. Spark is a distributed model of data processing developed to resolve the MapReduce limitations. It has been introduced as part of the Hadoop, and is designed to run with Hadoop, specifically by accessing HDFS data. This paradigm is based on resilient distributed datasets (RDDs) which is a particular form of data structure used to coordinate computations transparently. Spark offers a collection of in-memory operators in addition to the standard MapReduce to analyse data faster in distributed settings [35]. A number of research works on big data clustering with Spark paradigm were proposed. For instance, Zayani et al. [60] recommended the parallelization of overlapping k-means clustering technique based on the Spark paradigm (SOKM). The proposed technique can carry out parallel clustering processes that result to pre-disjoint data segmentation. Current DBSCAN parallel algorithms produce data partitions in which the initial data are usually split into many disjoint partitions. But by the increase in data dimensions, dividing and reorganizing huge dimensional space can take much time. In order to solve this problem, Luo et al. [61] propose a Spark-based parallel DBSCAN method (S-DBSCAN) that can rapidly split the data and combine the results of the clustering. The suggested work is broken down into three phases. The first phase is partitioning the set of data based on the strategy of random samples. The aim is to calculate the number of partitions based on the actual computing nodes, and on this basis, the original random data will operate through a custom random function. The original random data would be exporting to each slice. Slice of data points has approximately the same data volume and is similar to a simple random sampling. When the number of samples each slice extracts is large enough, it has a similar distribution with the original raw data. After the data is split, the local DBSCAN is calculated in parallel, so partial clustering results are produced. At the map task, partial cluster is created. The partial cluster is saved to HDFS as a new RDD, and at the reduced task each partial cluster centroid is computed. At the last point, the results of global clustering were created by fusing the partial cluster. They suggested a merger strategy based on the centroids. The idea is to measure the distance in the same partition between each of the two partial clusters, and use a sorting technique to determine the minimum distance. Moreover, it determines the minimum value by sorting the minimum distance. Thirdly, threshold is set to merge partial clusters. Finally, construct a matrix with a centroid distance

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and traverse every element in the matrix. If the distance is below the threshold then add it to the merge queue until each element is visited. The experimental results indicate a large speed-up of the S-DBSCAN. For the same number of nodes, the larger the dataset, the greater the speed-up. This completely illustrates the superior efficacy of the S-DBSCAN algorithm compared to other parallel DBSCAN algorithms when dealing with huge data. Ben HajKacem et al. [62] presented a k-prototypes clustering technique for massive-scale data with mixed attributes (KP-S). An RDD object with the input dataset from the m partitions of the dataset was generated to parallelize the k-prototype algorithm using Spark. To accomplish this, a Spark framework operation testfile() was used. The map task selects a partition of the dataset, performs the k-prototypes algorithm on that partition, and releases the intermediate centres extracted and their weights as the output. MapPartition() transformation was used in this work which runs the k-prototype algorithm separately on each RDD block. Upon completion of the map phase, a set of intermediate weighted centres is obtained as the map phase output and this collection of centres is released to a single reduce step. The reduction step takes the set of intermediate centres and their weights, performs the k-prototype method on them again, and releases the final centres as the output. ReduceByKey() transformation from the Spark framework was used to simplify implementation. When the final cluster centres are established, the closest cluster centre is assigned to each data point. The obtained results show that KP-S always completes many times faster than existing methods. For instance, with 100 clusters on the Poker dataset, the KP-S algorithm can decrease runtime by 94.47 and 85.72% in comparison to k-prototypes and k-prototype using MapReduce (KP-MR), respectively. In all of the analysis, over 95% of the running time is spent in the map process, which indicates that KP-S is actually performed in memory. An effective parallel density peak clustering technique using GraphX was implemented based on Spark technology to decrease high time complexity of density peak clustering method by Liu et al. [63]. This implementation is based on construction of graph, computing the truncated distance, computing the local density and computing the distance from higher density points. Construction of graph consists of three phases. First, importing vertex and edge data stored on HDFS or other file systems separately to vertex RDD and edge RDD, and setting each edge’s initial value to a constant. Secondly, measuring the distance of each edge based on a distance measure formula updating the value of each edge by the distance. Thirdly, the combination of vertex RDD and edge RDD in GrapgX to form a graph. When the vertex set and edge set are updated, the initial value for each edge is set to 1. When measuring the distance of each edge, the distance is changed to the value of each edge. To reduce the computation load, the truncated distance is measured before calculating the local density. After the truncated distance is measured, the local density and the distance from points of higher density is determined. Experimental result shows that the Spark implementation can improve significantly (10x) compared to MapReduce implementation. K-prototype is an iterative algorithm that requires some iteration for generating best results. Conversely, MapReduce has a significant issue with iterative algorithms. As a result, the entire dataset at each iteration must be loaded into the main memory from the file system. Then, the data must be written to the file system

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again after it is processed. Consequently, several I/O disk operations occur during each iteration, and this reduces runtime. To overcome this problem, Ben HajKace et al. [64] proposed a scalable random dampling k-prototypes implemented on the Spark paradigm. A reservoir random sampling technique was used for selecting random data sample from the original dataset. The data sampling MapReduce job first generates an RDD object with input data X form by m partitions. In the map phase, each partition is then processed to generate the intermediate data samples. The reduce phase then receives the set of intermediate data samples to produce the final data sample. The map function takes a partition during the map process and applies the random reservoir sampling algorithm to create an intermediate data sample of size r. The map function then releases the intermediate data samples as a single-phase reduce output. The reduce phase gathers the intermediate data samples generated in the map phase to create the final data sample. Upon generating the data sample, the second MapReduce job checks for cluster centres. First, it generates an RDD object with the data sample S form by m partition. Then, each partition is analysed in map phase to generate the intermediate centres. The reduction step afterwards processed the collection of intermediate centres to produce the final clusters. The map function selects a partition during the map process and executes the k-prototypes on that partition to obtain the cluster centres. To achieve good quality, for each obtained centre a weight that signifies the number of assign data points was recorded. The number of allocated data points per cluster centre reflects the centre’s importance. At last, the map function releases the intermediate centres extracted and their weights as the output to a single reduce step. In this implementation, mapPartition(func) transformation was used separately on every partition of the RDD. The reduce step takes the intermediate centres and their weights, runs the k-prototypes algorithm on them again, and returns the final centres as the output. ReduceByKey(func) transformation from the Spark framework was used to simplify the implementation. The experimental results obtained show that the method proposed often finishes many times faster than the classical k-prototypes and the k-prototype based on MapReduce. The method proposed was found to be 14 times faster than the classical kprototypes and four times faster than the MapReduce-based method. However, it was found that most of the running time was spent on the step of the map which indicates that the proposed method is actually being performed in memory. In the field of agricultural image segmentation, the fuzzy c-means (FCM) algorithm has been commonly used because it provides easy computation and high quality segmentation. However, the sequential FCM is too slow due to its large amount of computation to complete the segmentation task in an appropriate time. Liu et al. [65] suggest a parallel FCM segmentation algorithm based on the Apache Spark for clustering agricultural images. The input image is first converted to the Lab colour space from the RGB colour space and generates point cloud data. Point cloud data is then split and stored in separate computer nodes where the membership degrees of pixel points to separate cluster centres are determined and the cluster centres are modified iteratively in parallel form until the stop condition is met. In the RDD, point cloud data is finally restored after clustering to reconstruct the segmented image. The FCM output is measured on the Spark platform and achieves an average speed of 12.54

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on 10 computing nodes. Experimental results show that the Spark-based parallel fuzzy c-means algorithm can achieve substantial speed-up improvements, and the agricultural image test set provides a 128% better performance improvement than the Hadoop-based solution. This work shows that the Spark-based parallel FCM algorithm provides faster segmentation speed for big data on agricultural images and has better scale-up and size-up. Yu et al. [66] presented an effective three-way cluster ensemble approach based on Spark to solve the difficulty of clustering on large-scale data; this methodology has the potential to deal with both soft and hard clustering. A three-way cluster ensemble based on Spark was suggested, inspired by the principle of three-way decisions, and a distributed three-way clustering algorithm for k-means was developed. Also introduced was the concept of cluster unit, which represents the minimum structure of distribution of granularity decided upon by all members of the ensemble. This research also incorporates quantitative tests to determine the relationship between units and between clusters. Eventually, a consensus-based clustering algorithm was suggested, and several three-way decision strategies were developed to assign small cluster units and non-unit objects. The experimental tests using 19 real-world datasets were used with various metrics such as ARI, ACC, NMI and F1Measure to verify the feasibility of the proposed method. The experimental result shows that the proposed technique can manage large-scale data effectively, and the proposed consensus clustering algorithm has a lower time cost without loss of the consistency of the clusters.

4 Big Data Clustering Applications Most applications in the real world generate vast amounts of data. Some of these are (but not restricted to) healthcare applications, IoT applications, detection of anomalies. Here we present a list of applications for big data clustering as shown in Fig. 3.

Fig. 3 Taxonomy of big data clustering applications

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4.1 Healthcare Several researchers have used different forms of clustering strategies for big data in occupational medicine [67], acute inflammation [68], and grouping of cancer samples [69]. McParland and Gormley [70] and Mcparland et al. [71] develop a clustering algorithm for studying high-dimensional categorical genotypic and numerical phenotypic data. The research contributes to a greater understanding of metabolic syndrome. Narmadha et al. [72] use a hybrid fuzzy k harmonic means (HFKHM) for the categorization of tumours as benign or malignant. Su et al. [73] also employs a hierarchical clustering to continually group respondents based on similarity structures of 27 different factors of asthma symptoms and 14 different product applications.

4.2 Internet of Things (IoT) Amini et al. [74] suggested a hybrid clustering algorithm built on density-based clustering method for the IoT streaming data. The proposed method consists of three phases under which the new data point is connected to a grid or embedded into a current mini cluster, the outliers are eliminated and arbitrary shape clusters are formed through the use of an enhanced DBSCAN. A new data clustering framework is used for big sensory data generated by IoT applications by Karyotis et al. [75]. This framework can also be used for community detection and performing more energyefficient smart-city/building sensing. Clustering algorithms have been widely used in IoT applications such as looking for similar sensing patterns, identifying outliers, and dividing massive real-time behavioural groups [76].

4.3 Anomaly Detection Fanaee-T and Gama [77] uses an expectation maximization to model an anomaly detection using structural time series for industrial Ethernet congestion into four sections cantered on a method with a specific significance for detection. This model helps to improve the efficacy of identifying irregular and low false alarm levels. A new developed clustering algorithm was also used in the work of Alguliyev et al. [78] for detection of anomalies. Yin et al. [79] used a self-organizing maps to detect anomalous risk in mobile apps

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4.4 Social Media Alsayat and El-Sayed [80] use an improved k-means algorithm to identify communities by clustering posts from huge streams of social data. In the work of Gurusamy et al. [81], k-means algorithm is also used to exploit social network users’ attitudes. On the basis of textual similarity, spectral k-means clustering is used to group people in social network by Singh et al. [82]. A varied density-based spatial clustering for twitter data algorithm that extracts clusters from geo tagged twitter posts using heterogeneity in space. The algorithm uses incremental spline interpolation to ascertain various cluster detection search radii [83]. They were successfully tested for event detection in social media using geo tagged twitter post received in the course of a storm in the United States.

5 Discussion Parallel methods for big data clustering have been categorized in this work into four GPU-based, MPI-based, MapReduce-based, and Spark-based methods groups. But before applying clustering to any of the parallel frameworks, consideration of certain challenges for each of the framework is necessary. GPU suffers from limited memory. If the data size exceeds the GPU memory size, the performance reduces the GPU-based approach significantly. For example, it is not acceptable to handle terabyte data with a maximum of 12 GB of memory per GPU. Moreover, MPI has no fault control function at all. One system failure in the network will cause the entire network to shut down. Practitioners also need to incorporate some kind of fault tolerance system inside the software to resolve mistakes. The MapReduce architecture looks better than MPI as it is defined by a straightforward structure for programming, linear scalability, and tolerance of faults. It is, however, inappropriate to run iterative algorithms, because the entire dataset must be read and written to disks at each iteration and this results in high (I/O) operations. This significantly degrades the performance of MapReduce-based method. Finally, Spark framework is an alternative to MapReduce which is designed to overcome the disk I/O limitations and improve the performance of MapReduce framework. A recent survey outlined the various frameworks for big data analytics and presents the advantages and drawbacks of each of these frameworks based on different metrics such as scalability, data I/O rate, fault tolerance, and iterative task support [84, 85]. While most of the big data partition clustering methods described in this work provide an efficient analysis for large-scale data for users, some parameters have to be calculated before the learning is finished. The methods include configuring the number of clusters in advance, which is not a trivial task in real-life applications where the number of clusters predicted is typically unknown. One could use various model heuristics as a solution to determine the optimal number [86, 87]. For instance, with an increased number of clusters, the user may check various clusters and then take the

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clustering that has the best balance between minimizing the objective function and number of clusters. In addition, the partitioning methods must initialize the centres of the clusters. However, initialized centres of high quality are critical, both for the accuracy and performance of traditional clustering methods. In order to overcome this problem, users may use random sampling methods to obtain cluster centres or initialization techniques that exploit the fairly widespread use of good clustering [88– 90]. The outcome of the described methods, using centre initialization techniques, often converges to a local optimum of the objective criterion, rather than the global optimum. To resolve this problem, users should combine traditional methods with heuristic techniques to avoid optimum local clustering results [3, 21, 29, 91–93].

6 Challenges and Future Research Work Despite the growth in these technologies and algorithms to handle big data, there are still challenges such as: • Scalability and storage issues: Data rises are much higher than existing computing systems. Storage devices are not adequately capable of processing these data [94– 97]. A processing system needs to be built which not only addresses the needs of today but also future needs. • Analytical timeliness: Data value decreases over time. Many applications, such as telecom, insurance, and banking fraud detection, require real-time or near-realtime transactional data processing [94, 95]. • Heterogeneous data representation: Data derived from different sources are of heterogeneous type. It is difficult to store and process unstructured data such as images, videos, and social media data using conventional methods such as SQL. Smartphones are now capturing and exchanging images, audios, and videos at an exponentially higher pace, pushing our brains to further process. However, there is a lack of effective storage and processing of the medium for representing pictures, audios, and videos [94, 95, 98]. • Privacy and security: New devices and technologies like cloud computing provide a gateway to access and to store information for analysis. This integration of IT architectures will pose greater risks to data security and intellectual property. Access to personal information like buying preferences and call detail records will lead to increase in privacy concerns [96, 99]. Researchers have technological infrastructure to access data from any source of data, including social networking sites, for potential use, although users are unaware of the benefits that can be made from the information they post [100]. The disparity between privacy and convenience is unknown to big data researchers. Based on this review, the following future works have been formulated.

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• Hybrid approaches that combine multiple acceleration techniques showed great promise for big data clustering. For the development of a reliable hybrid approach for big data clustering, further work is needed, however. • Due to the effectiveness of the big data parallel methods, a method for the parallelization of the hybrid methods is highly needed.

7 Conclusion This paper offers an in-depth analysis of the big data clustering algorithms to guide the selection of the big data algorithms. We also include categorization of various big data clustering research works. At the end, we describe various applications where big data clustering was used. In sum up, though conventional sampling and dimension reduction algorithms are still useful, but they don’t have enough power in managing massive quantities of data because even after sampling a petabyte of data, it is still very large and clustering algorithms cannot cluster it, so the future of clustering is related to distributed computing. Although parallel clustering is potentially very useful for clustering, but the complexity of implementing such algorithms is still a challenge.

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A Survey of Network Intrusion Detection Using Machine Learning Techniques Thomas Rincy N and Roopam Gupta

Abstract Nowadays, a huge amount of information flows daily on public and private computer networks. Since sensitive information has a high probability of being transmitted, there is an important need to protect networks from intrusions. Hence, adopting an intrusion detection system is imperative. As the frequency of sophisticated attacks has been increasing tremendously over the past years, machine learning approaches were introduced to identify intrusion patterns and prevent sophisticated attacks. This survey provides an up-to-date review of leading-edge techniques used by intrusion detection systems that rely on machine learning techniques. Moreover, it introduces important key machine learning concepts such as ensemble learning and feature selection that are applied to protect networks from unauthorized access and make networks and computers safer. The article then reviews signature, anomaly, and hybrid intrusion detection systems that apply machine learning techniques. It is observed that hybrid network intrusion detection system may be the most effective. Then, the article examines the characteristics of popular benchmark datasets for evaluating intrusion detection systems such as NSL-KDD, Kyoto 2006 +, and KDD Cup-‘99 and performance metrics to appraise intrusion detection results. Finally, the article discusses research opportunities in the field of intrusion detection. Keywords Intrusion detection system · Machine learning · Ensemble learning · Feature selection · Datasets · Performance metrics

Thomas Rincy N (B) Department of Computer Science and Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, MP, India e-mail: [email protected] Roopam Gupta Department of Information Technology, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, MP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_4

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1 Introduction In recent decades, the internet has greatly facilitated communication between individuals and businesses. Many services are now offered and a lot of important and confidential information is transmitted over the internet and private computer networks. In this context, an important issue is to protect the privacy and integrity of computer networks from intruders and to ensure that networks can continue to operate normally when they are under attack. A recent report by Cybersecurity Ventures estimated that by 2021, cybercrime damage will rise to $6 trillion annually from $3 trillion in 2015 [1]. Moreover, it is predicted that there will be 6 billion internet users worldwide by 2022, and global spending on cyber security will cumulatively exceed $1 trillion over the next five years. Due to the increasing frequency of sophisticated cyber-attacks, network security has become an emerging area of research for computer networking. To prevent intrusions and protect computers, programs, data, and unauthorized access to systems, intrusion detection systems (IDS) have been designed. An IDS can identify an external or internal intrusion in an organization’s computer network and raise an alarm if there exists a security breach in the network [2]. To allow IDS to operate automatically or semi-automatically, machine learning techniques have been employed. Machine learning [3] can detect the correlation between features and classes found in training data, and identify relevant subsets of attributes by feature selection and dimensionality reduction, then use the data to create a model for classifying data to perform predictions. The rest of this article is organized as follows. Section 2 succinctly introduces machine learning concepts and techniques. Section 3 reviews machine learning based intrusion detection system and in particular discusses the importance of ensemble learning and feature selection techniques. Section 4 concentrates on hybrid intrusion detection systems. Section 5 describes the NSL-KDD, Kyoto 2006 + , and KDD Cup‘99 benchmark datasets and the various performance metrics to evaluate the IDS. Section 6 discusses the research opportunities. Finally, Sect. 7 draws a conclusion.

2 Machine Learning Machine learning is prominently applied in the area of intrusion detection systems. It is the field through which the various computer algorithms are studied, that improves incrementally through the experience and has been an area of computational learning theory and pattern recognition in artificial intelligence research since the field of its inception [4]. It helps in predictions and decisions on data, by studying algorithms and building a model from the input data. Machine learning is classified into supervised learning, semi-supervised learning, un-supervised learning, and reinforcement learning [5]. Figure 1 shows the classification of machine learning types with their corresponding methods.

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Supervised Learning

Un-Supervised Learning

Semi-Supervised Learning

Reinforcement Learning

Logic Based Algorithms

K-Means Clustering

Generative Models

Adaptive Heuristic Critic Algorithm

Perceptron Based Techniques

Gaussian Mixture Model

Transductive Support Vector Machine

Q-Learning

Statistical learning Algorithms

Hidden Markov Model

Graph Based Methods

Model Free Learning

Linear Discriminant Analysis

Self-Organizing Map

Self-Training Methods

Certainty Equivalent Methods

Support Vector Machine

Co-Training Methods

Dyna

Prioritized Sweeping/ Queue-Dyna Real Time Dynamic Programming

Fig. 1 Classification of machine learning system

2.1 Supervised Learning Supervised learning is a machine learning task that assumes a function from the labeled training data. In supervised learning, there is an input variable (P) and an output variable (Q). From the input variable, the function of the algorithm is to study the mapping function to the output variable Q = f(P). Spervised learning aims to analyze the training data then produces a complete function that can be utilized to map the new instances. The learning algorithm should be able to analyze and generalize those class labels correctly from the unobserved instances. This section introduces the various algorithms used in supervised learning.

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Logic Based Algorithms

This subsection describes the decision trees and rule-based classifiers.

Decision Trees A decision tree [6] is a directed tree with a root node having no incoming edges. The remaining node has an incoming edge. Each of the leaf nodes is available with a label, non leaf nodes have a feature which is called a feature set. According to the distinct values in the feature set, the decision tree splits the data that falls inside the non leaf node. The testing of the feature is operated from the leaf node and its outcome is achieved until the leaf node has arrived.

Rule-Based Classifiers Quinlan et al. [7] stated that, by transforming the decision tree into a distinct set of rules, a different path can be created for the set of rules. From the root of the tree to the leaf of the tree, a distinct rule for each path is created the decision tree is altered in to set of rules. Directly, from the training data, the rules can be inducted by different algorithms and apply these rules. The main goal is to construct the smallest set of rules that is similar to the training data.

2.1.2

Perceptron Based Techniques

Perceptron algorithm is applied to the batch of training instances for learning. The main objective is to run the algorithm frequently among the training data, till it discovers a prediction vector that is appropriate on all of the training data. For predicting the labels, this prediction rule is then applied to the test set [8]. The following subsection introduces perceptron based techniques.

Neural Network The neural network conceptual model was developed in 1943 by McCulloch et al. [9]. It consists of different cells. The cell receives data from other cells, processes the inputs, and passes the outputs to other cells. Since then, there was intensive research to develop the ANNs. A perceptron [10] is a neural network that contains a single neuron that can receive more than one input to produce a single output.

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A linear classifier contains a weight vector w and a bias b. From the given instance x, the predicted class label y is obtained according to y = sign(wT x + b) With the help of weight vector w, the instance space is mapped into a onedimensional space, afterwards to isolate the positive instances from negative instances.

2.1.3

Statistical Learning Algorithms

In statistical learning, there is a likelihood of a probability where each instance belongs to its class. The following subsection describes statistical learning algorithms.

Bayesian Network Bayesian Network is the directed acyclic graph (DAG) [11], in which each edge correlates to the conditional dependency and each node correlates to a distinctive random variable. The Bayesian network performs the following task for learning. Firstly it learns the directed acyclic graph of the network. Secondly, the aim is to find the network parameters. Once the parameters or probabilistic parameters are found, then the parameters are induced in the set of tables, and joint distribution is remodeled by increasing exponentially the tables.

Naive-Bayesian Classifier Naive Bayesian classifiers [12] are probabilistic classifiers with their relation related to Bayes theorem having a strong assumption of naive independence among its features. Bayes theorem can be stated in mathematical terms: P(A/B) = P(A)(B/A)/P(B) where A and B are events. P(A) and P(B) are events. P(A) and P(B) are the prior probabilities of A and B.P(A/B), is a posterior probability, of the probability of observing an event A, provided that B is true. P (B/A) is known as likelihood, the probability of observing an event B provided that A is true. Naive Bayes classifier has the advantage of requiring the least execution time required for training the data.

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K-Nearest Neighbor Classifiers K-NN [13] is a nonparametric technique applied for regression and classification. In the feature space, the input of k-NN contains the k closest training examples. Then the output will depend on whether k-NN is applied for regression or classification purposes. The output is the property value for the object in k-NN regression.

2.1.4

Linear Discriminant Analysis

Linear discriminant analysis is a method to find the linear combination of the features that separate the two or more classes of objects. The resulting combination can be used as a linear classifier. A linear classifier [14] contains a weight vector w and a bias b. From the given instance x, the predicted class label y is obtained according to y = sign(wT x + b) With the help of weight vector w, the instance space is mapped into a one-dimensional space, afterward to isolate the positive instances from negative instances.

2.1.5

Support Vector Machine

SVM [15] revolves around the margin on either side of a hyperplane that separates two data classes. To reduce an upper bound on the generalization error, the main idea is to generate the largest available distance between its instance on either side and separating hyperplane. The data points that lie on the margins of the optimum hyperplane are termed as Support Vector Points, and it is characterized as the linear combination of these points. An alternative data point is neglected. The different features available on the training data do not affect the complexity of SVM. This is the main reason the SVM is employed with learning tasks having a large number of features with respect to the number of training data. Large problems of the SVM cannot be solved as it not only contains the large quadratic operations but also there exist numerical computation which makes the algorithm slow in terms of processing time. There is a variation of SVM called Sequential Minimal Optimization (SMO). SMO can solve the SVM quadratic problem without employing any extra matrix storage and without using any numerical quadratic programming optimization steps [16].

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2.2 Un-Supervised Learning In Un-Supervised learning the data is not labeled, more precisely, there are unlabeled data. Un-Supervised learning consists of an input variable (P), but there is no output variable. The representation is seen as a model of data. Un-Supervised learning aims to obtain the hidden structures from unlabeled data or to infer a model having the probability density of input data. This section reviews some of the basic algorithms used in Un-Supervised learning.

2.2.1

K- Means Clustering

K-means clustering [17] partitions the N observation in space into K clusters. The information and nearest mean belongs to this cluster and works as a model of the cluster. As a result, the data space splits into Voronoi cells. K-means algorithm is an iterative method, which starts with a random selection of the k-means v1 , v2 … vk . With each number of iteration the data points are grouped by k-clusters, keeping with the closest mean to each of the points, mean is then updated according to the points within the cluster. The variant of K-means is termed as K-medoids. In K-medoids, instead of taking the mean, the larger part of the cluster having the centrally located data point is investigated as a reference point of the corresponding cluster [18].

2.2.2

Gaussian Mixture Model

The Gaussian mixtures were popularized by Duda and Hart in their seminal 1973 text, Pattern Classification and Scene Analysis [19]. A Gaussian Mixture is a function that consists of several Gaussians, each identified by k ∈ {1,…, K}, where K represents the number of clusters of a dataset. Each Gaussian is represented by the combination of mean and variance, consisting of a mixture of M Gaussian distributions. The weight of each Gaussian will be a third parameter related to each Gaussian distribution in a Gaussian mixture model (GMM). When clustering is performed applying Gaussian Mixture Model, the goal is to obtain a criterion such as mean and covariance of each distribution and the weights, so that the resulting model fits optimally in the data. To optimally fit the data, one should enlarge the likelihood of the data given in the Gaussian Mixture Model. This can be achieved by using the iterative expectation maximization (EM) algorithm [20].

2.2.3

Hidden Markov Model

HMM [21] is a parameterized distribution for sequences of observations. A hidden Markov model is a Markov process that is divided into two components called observable components and unobservable or hidden components. That is, a hidden Markov

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model is the Markov process (Y k , Z k ) k ≥ 0 on the state space C × D, where we presume that we have a means of observing Y k , but not Z k as the signal process and C as the signal state space, while the observed component Y k is called the observation process and D is the observation state space.

2.2.4

Self-organizing Map

A self-organizing map (SOM) [22] is a form of an artificial neural network, trained by applying unsupervised learning to yield a low dimensional, discretized representation of input space of the training samples called maps. SOM applies the competing learning while ANN applies the error-correction learning (backpropagation with gradient descent). To retain the topological properties of the input space the SOM applies the neighborhood function. SOM begins with initializing the weight vectors. Sample vectors are chosen randomly and the map of the weight vectors is inspected to find the best weight that represents that sample. The weight vector has neighboring weights that are close to each other. The chosen best weight sample and neighbors of that weight are rewarded and is more likely to become the randomly selected sample vector. This strategy allows the maps to grow and take the different shapes such as rectangular, square, and hexagonal in two-dimensional feature spaces.

2.3 Semi-supervised Learning Semi-Supervised learning is the sequence of labeled and unlabeled data. The labeled data is very sparse while there is an enormous amount of unlabeled data. The data is used to create an appropriate model of the data classification. Semi-Supervised learning aims to classify the unlabeled data from the labeled data. This section explores some of the most familiar algorithms used in Semi-Supervised learning.

2.3.1

Generative Model

Generative model [23] considers a model p(x, y) = p(y) p(x/y) where p(x/y) is known as mixture distribution. The mixture components can be analyzed when there are large numbers of unlabeled data is available. Let { pθ } be the distribution family and is denoted by parameter vector θ . θ can be identified only if θ1 = θ2 ⇒ pθ1 = pθ2 to a mixture components transformation. The expectation–maximization (EM) algorithm is applied on the mixture of multinomial for a task of text classification [24].

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Transductive Support Vector Machine

TSVM [25] extends the Support Vector Machine (SVM) having the unlabeled data. The idea is to have the maximal margin among the labeled and unlabeled data on its linear boundary by labeling the unlabeled data. Unlabeled data has the least generalization error on a decision boundary. The linear boundary is put away from the dense region by the unlabeled data. With all the available estimation solutions to TSVM, it is curious to understand just how valuable TSVM will be as a global optimum solution. Global optimal solution on small datasets is found in [26]. Overall an excellent accuracy is obtained on a small dataset.

2.3.3

Graph Based Methods

In Graph based methods [27] labeling information of each sample is proliferated to its neighboring sample until a global optimum state is reached. A Graph is constructed with nodes and edges where nodes are defined with labeled and unlabeled samples, while the edges define the resemblance among labeled and unlabeled data. The label of the data sample is advanced to its neighboring points. The graph based techniques are the focus of interest among researchers due to their better performance. Kamal and Andrew [28] propose semi-supervised learning as the graph mincut (known as st-cut) problem. Szummer et al. [29] performed a step Markov random walk on the graph.

2.3.4

Self Training Methods

Self-training is a methodology applied in semi-supervised learning. On a small amount of data, the classifier is trained and then applied to classify the unlabeled data. The most optimistic unlabeled points, together with their labels predicted, are then added to the training set. The classifier is again trained with the training dataset. This procedure goes on repeating itself. For teaching itself, the classifier had its own predictions. This methodology is called bootstrapping or self-teaching [30]. Various natural language processing tasks apply the methodology of self teaching.

2.3.5

Co-Training

Co-Training [31] is applied where there are a large amount of unlabeled data and a small amount of labeled data. In Co-Training, there is a general assumption that the features can be divided into feature subsets. Every feature subset is adequate for training the classifier, and for the given class the feature sets are relatively independent.

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There are two classifiers primarily that are trained on a labeled data on the pair of feature subsets. With labeled and unlabeled data, each classifier trains the other classifier to retain the supplementary training sample from another classifier and this process gets repeated often until optimal features are found.

2.4 Reinforcement Learning In reinforcement learning, the software agent gathers the information from the interaction, with the environment to take actions that would maximize the reward. The environment is formulated as a markov decision process. In reinforcement learning, there is no availability of input/output variables. The software agent receives the input i, the present state of environment s, then the software agent determines an action a, to achieve the output. The software agent is not told which action would be best in terms of long term interest. The software agent needs to gather information about the states, actions, transition, rewards for optimal working. This section reviews algorithms used in reinforcement learning.

2.4.1

Adaptive Heuristic Critic

The adaptive heuristic critic algorithm is a policy iteration, adaptive version [32] is implemented by Sutton’s TD(0) algorithm [33]. Let ((a, w, t, a’)) be the tuple representing a single transformation in an environment. Let a, be the state of an agent before its transition, w is the action of its choice, t is the award it receives instantaneously, and a’ is culminating state. The policy value is studied by Sutton’s TD(0) algorithm which utilizes the update rule given by: V (a) := V (a) + w(t + t V (a  ) − V (a)). When the state a is visited, its estimated value is amended to be adjacent to (t + tV (a’) since t is the spontaneous reward received and V(a’) is the approximate rate of the next appearing state.

2.4.2

Q-Learning

Q-learning [34] is a type of model free reinforcement learning. It may also be known as an approach of asynchronous dynamic programming (DP). Q-learning allows the agents to have the ability to learn and to perform exemplary in the markovian field by recognizing the effects of its actions, which is no longer required by them to build domain maps. Q-learning finds an optimal policy and it boosts the predicted

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value of the total reward, from the beginning of the current state to any and all successive steps, for a finite markov decision process, given the infinite search time and a partially random policy. An optimal action-selection policy can be associated with Q-learning.

2.4.3

Certainty Equivalent Methods

The certainty equivalent method [35] is studied continuously throughout the agent lifespan. The present model is applied to calculate the optimum policy and functional value. The certainty model effectively uses the available data on rather side, it is computationally costly even for small state areas.

2.4.4

Dyna

Suttons Dyna Architecture [36] applies a yielding approach that tends to be more successful and is more computationally effective than certainty-equivalent methods. Markov decision process is defined by a tuple (a, w, t, a’) and it acts as follows: The model gets transited state from a to a’ upon on action and getting the reward t by taking action w in the state a. Updated models are then M and P. Dyna updates the state a by applying the rules that are an adaptation of the value iteration updating for the Q rules.

2.4.5

Prioritized Sweeping

The limitations with Dyna are proportionately undirected. When the target has just been reached it goes on to update its random state action pairs, instead of concentrating on the curious part of the state space. Prioritized sweeping addresses this problem by choosing the updates at random, the values are associated with space rather than state-action pairs. Additional information has to be stored in the model for having the fitting choices. Every state remembers its previous state, having the non zero transition probability. The priority of each state is set initially to zero. Prioritized sweeping updates the k states having the highest priority, rather than updating the k random state action pairs [37].

2.4.6

Real Time Dynamic Programming

The RTDP [38] applies Q learning. The computing is done on the areas, where the agent is coherent to occupy the state space. RTDP is specific to problems in which the agent tries to attain the particular goal state and the reward everywhere else is 0. RTDP finds the shortest path from the start state to the target state without visiting the unnecessary state space.

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2.5 Ensemble Learning Ensemble learning [39] is an imperative study in the domain of Machine Learning, Data mining, Pattern Recognition, Neural networks, and Artificial Intelligence. The goal of ensemble learning methods is to build the set of learners or classifiers then merge them so that it can make an accurate prediction, in contrast to single learner the accurate prediction may not be good as a set of learners. There are several learners in an ensemble learning called base learners. The goodness of ensemble learning is that they are capable of boosting the base learners. The base learners are sometimes called the weak learners. Therefore the ensemble learning is also called as learning of multiple classifier systems. In this section, few important concepts of ensemble learning techniques are discussed.

2.5.1

Boosting

Boosting [40] is an ensemble learning paradigm, which builds a strong classifier from the number of base learners. Boosting works sequentially by training the set of base learners to combine it for the prediction. The boosting algorithm takes the base learning algorithms repeatedly, having the different distributions or weighting of training data on the base learning algorithms. On running the boosting algorithm, the base learning algorithms will generate a weak prediction rule, until many rounds of steps the boosting algorithm combines the weak prediction rule into a single prediction rule that will be more accurate than the weak prediction rule.

2.5.2

Bagging

Bootstrap AGGregatING [41]. Bagging algorithms combine the bootstrap and aggregation and it represents parallel ensemble methods. Bootstrap sampling [42] is applied by bagging to acquire the subsets of data for training the base learning algorithms. Bagging applies the aggregating techniques such as voting for classification and averaging for regression. Bagging uses a precedent to its base classifiers to obtain its outputs, votes its labels, and then obtains the label as a winner for the prediction. Bagging can be applied with binary classification and multi-class classification.

2.5.3

Stacking

In Stacking [43] the independent learners are combined by the learner. The independent learners can be called cardinal learner, while the combined learner is called Meta learner. Stacking aims to train the cardinal learner with initial datasets to generate the contemporary dataset to be applied to the Meta learner. The output generated by the cardinal learner is regarded as input features, while the original labels are still

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Base Learner. 1 Base Learner. 2 P

Combination of base learner

…..

Q

Base Learner. n

Fig. 2 A basic architecture of ensemble learning

regarded as labels of the contemporary dataset. The cardinal learner is obtained by applying different learning algorithms, often generating composite stacked ensembles, although we can construct uniformly stacked ensembles. The contemporary dataset has to be obtained by the cardinal learner otherwise, the dataset is the same for the cardinal and Meta learner there is speculation of over fitting. Wolpert et al. [44] emphasized various features for the contemporary dataset and categories of learning algorithms for the Meta Learner. Figure 2 shows the basic architecture of ensemble learning.

2.6 Feature Selection Feature selection is a well-studied research topic in the field of artificial intelligence, machine learning, and pattern recognition. Feature selection removes the redundant, irrelevant, and noisy features from the original features of datasets by choosing the relevant features having the smaller subdivision of the dataset. By applying the various techniques of feature selection to the datasets, results in lower computational costs, higher classifier accuracy, reduced dimensionality, and a predictable model. Existing feature selection methods for machine learning fall into two broad categories. The learning algorithms that classify relevant and useful features applied to the data are termed as the wrapper method and those evaluate the merit of features by using heuristics based on general characteristics of data are termed as filter method [45]. This section focuses on the characteristics of feature selection algorithms, introduces the filter and wrapper approaches.

2.6.1

Characteristics of Feature Selection Algorithms

The feature selection algorithm does its task by performing the exploration in the space having feature subsets, and as a result, should define the four fundamental problems influencing the variety of the search [46].

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• Starting point. The selection of points in a feature subset space affects the direction of the search. There are different choices to begin the search. The search space can start with no features to successfully adding the attributes. Within the search space, the search moves forward, the subsequent possibility is that the search space begins with all features and adequately removes them. Another alternating technique is to search the space in the middle and then moves outwardly from this point. • Search organisation. Whenever there is data having the features containing the larger number, the searching of the feature subset within the space and time frame is decisive. There exists a 2 N possible subsets for the N number of features. As compared to exhaustive search techniques the heuristic search techniques are more practicable. Greedy hill climbing is a strategy that considers the single feature that can consecutively be added or deleted from the current feature subset. Whenever the algorithm favors the inclusion and elimination of the feature subset then it is termed as forward selection and backward elimination [47]. • Evaluation strategy. The biggest challenge among the feature selection algorithms for machine learning is the classification of feature subsets [48]. In this strategy, the algorithm eliminates the undesirable features before the learning starts. The algorithm applies the data having a heuristic based on general characteristics to calculate the goodness of the feature subset. When selecting the features, the bias of the induction algorithm should be considered. This method is called Wrapper, which applies an induction algorithm, with re-sampling techniques to obtain the accuracy of the feature subsets. • Stopping criterion. An important decision of feature selection is to determine when to terminate the searching space of feature subsets. A feature selector should terminate the searching space of feature subsets by summing or eliminating the features when the feature selector finds no one of its substitutes. It enhances the quality of a prevailing feature subset. Concurrently, the algorithm may consider carrying on, altering the feature subset until the quality of the feature subset does not degrade. 2.6.2

Filter Methods

The earlier approaches that were introduced to feature selection are filter methods. Filter methods apply the data that has an examining property in general to calculate the goodness of feature subset, except a learning algorithm that evaluates the quality of the feature subsets. Filter algorithm executes many times faster than wrapper so there is a high probability of scaling the databases having a large number of features. Filter does not require the re-execution for the different learning algorithms. This strategy makes filter approaches quicker than wrapper methods. There are major drawbacks of the filter methods such as some filter algorithm is unable to handle the noisy data, some algorithms need the user to specify the level of noise, and in some

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Testing Data

Training Data

Search Training Data

Feature Set

Feature Evaluation

Heuristic Search

Dimensionality Reduction

Feature Set Training Data Machine Learning Algorithms

Filter

Hypothesis Final Evaluation Estimated Accuracy

Fig. 3 A filter feature selector

cases, the features are ranked and the final choice is left to the user. In other cases, the users need to specify the required number of features. Filter methods are applied mainly where the data is having highly dimensionality rate. Figure 3 shows the filter feature selector.

2.6.3

Wrapper Methods

For the selection of best feature subsets, a wrapper method applies an induction algorithm for the feature selection. The idea of wrapper approaches is that since induction methods are using the feature subset it should enhance the accuracy than a separate measure that applies distinct inductive bias [49]. Wrapper approaches are known to obtain better results than filter methods because they are specifically adapted to the interaction between the training data and induction algorithm. Whereas the wrapper methods are much slower than filter methods as the wrapper methods, frequently call the induction algorithm and reruns when a different induction algorithm is applied. Figure 4 shows the wrapper method.

2.6.4

Principal Component Analysis

Principal component analysis (PCA) [50] is an analytical procedure that converts the correlated variables into linearly uncorrelated variables, with the help of an orthogonal transformation. These are named as principal components. The PCA is a multivariate dimensionality reduction tool that extracts the features representing much of

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Testing Data

Training Data

Dimensionality Reduction

Search Training Data

Estimated Accuracy

Feature Set Feature Evaluation: Cross Validation Feature set + CV fold

Feature Set

Training Data Machine Learning Algorithms

Hypothesis Machine learning Algorithm

Hypothesis Final Evaluation

Wrapper Estimated Accuracy

Fig. 4 A wrapper feature selector

the features from the given data and thus removing the unimportant features having less information without losing the crucial information in data. When real data is collected, the random variables describing the data attributes are presumed to be highly correlated. The correlation between random variables can be found in the covariance matrix. The aggregate of the variances will give the overall variability.

2.6.5

Classification and Regression Trees

CART [51] is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly. The whole learning samples contains at the beginning with the root node. The basic idea behind the tree growth is to choose a split among all the possible splits at each node so that the resulting child nodes obtained are the purest. CART algorithm considers the univariate splits i.e. each split depends on the value of one predictor variable. Classification analysis of the tree is performed when the predicted outcome is the class to which the data belongs, while the regression analysis of the tree is done when the predicted outcome can be considered as a real number.

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3 Machine Learning Based Intrusion Detection System 3.1 Intrusion Detection System (IDS) The IDS literature history starts with a paper by James Anderson [52]. The main challenges of the organization are to protect the information from network threats. A string of actions through which intruder gains control of the system [53]. The main aim of intrusion detection is to detect previously known and unknown attacks, to learn and comply with new attacks and detect intrusions in an appropriate period. IDS are classified according to three main approaches: Implementation, Architecture, and Detection Methods [54]. Figure 5 shows the classification of the Intrusion Detection System.

3.1.1

Implementation Classification

The Intrusion Detection System is mainly classified according to Host-Based Intrusion detection System (HIDS) and Network-Based Intrusion Detection Systems (NIDS).

Host-Based Intrusion Detection Systems HIDS positions the sensors at an individual host of the network sensor. It collects the relevant information about data, based on various events. The sensor also records the data related to system logs, registry keys, and other logs generated by the operating system process called inspection logs or audit trail and any other unauthorized action. The HIDS rely heavily on inspection logs, degrading the performance of hosts related to bandwidth which can lead to high computational costs. The HIDS is better known IDS

Implementation

Host Based

Network Based

Architecture

Centralized

DeCentralized

Fig. 5 Classification of intrusion detection system

Detection

Distributed

Signature Based

Anomaly Based

Hybrid Based

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to trace malicious activities regarding specific users and also keeping a track record of the behavior of individual users within an organization.

Network Based Intrusion Detection Systems It collects the information from networks and checks for attacks or irregular behavior by examining the contents and header information and flow information of all packets across all networks. NIDS collects the information from networks examines the contents and the header information regarding all packets in various networks and then validates the irregular behavior of the network by verifying the various contents and header information of various packets whether there is an attack or not. The network sensor is having various sensors, a with built-in mechanism with attack signatures that define rules. These sensors also allow the user to define their signature for verifying the new attacks in the networks. NIDS is computationally costly and also time-consuming, as it has to inspect each flow and header level of packets from various networks, on the other side the NIDS is operating system and platform-independent that does not require any modification when NIDS operates. This makes NIDS more scalable and robust compared with HIDS.

3.1.2

Architectural Classification

The architectural classification of IDS is classified into three categories:

Centralized IDS The analysis and detection of monitored data are analyzed inside the CPU. There are various networks and different sensors present inside the centralized IDS. Inside the centralized IDS, different sensors monitor the data that are sent from various networks. Whenever there is a need for expansion of the network, the central processing unit is overloaded that is the major disadvantage of centralized IDS.

Decentralized IDS The CPU overloading problem is solved in decentralized IDS architecture. The multiple sensors and multiple processing units are scattered across the network. The gathered data are send to the nearest processing unit for getting processed, before they are sent to the main processing unit.

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Distributed IDS In distributed IDS the work load of the CPU is distributed among all its peers. The multiple IDS are distributed over the large network and its fundamental principle is that the multiple intrusion detection systems will communicate through peer to peer architecture.

3.1.3

Detection Methods

The detection methods of intrusion detection systems are classified into three major types: Signature-based, Anomaly-based and Hybrid-based. This section discuss various machine learning approaches used in signature, anomaly and hybrid IDS.

Signature-Based IDS The signature-based intrusion detection system stores the previously known or existing signature in a database and it looks for malignant activities and malignant packets in the network. It measures the similarity and patterns of the attacks within the existing signature that is saved in the database. If the similarity or pattern is found then an alarm is generated. Signature-based IDS are accurate in detecting the previously known attacks, but they cannot recognize the new attacks. Some of the machine learning algorithms that were used in signature-based IDS are Bayesian network [55], Decision Trees [56], SVM [57], ANN [58], K-NN [59], and Graph-based approaches [60].

Anomaly-Based IDS In anomaly-based IDS new action profiles are created and are applied to analyze the outliers that diverge from the profiles, Chandola et al. [61]. Anomaly-based intrusion detection system depends on analytical techniques to construct the various attack forecast models. It has the leverage of detecting anonymous attacks that do not have the extant signature in the database, but the major disadvantage is creating new action profiles. Sometimes the outliers that diverge from new action profiles are not always an attack, this creates incorrectly classified as an attack or False Positive. Some of the anomaly-based IDS that uses various machine learning techniques are Probability-based technique [62], Fuzzy rule classifiers using NN technique [63], Neural Network using back propagation technique [64], One class support vector machine [65], and K- NN [66].

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Network Packet

Sensors

Analyzer Management Server/ Analyzer Decision Making

Database Server

Fig. 6 A basic architecture of intrusion detection system

Hybrid-Based IDS The hybrid-based IDS combines both signature and anomaly-based detection approaches to detect attacks. The main advantage of a hybrid intrusion detection system is that it combines the existing features from the anomaly and signaturebased IDS. The downside of hybrid-based techniques is its increased execution time of applying both matchings of signatures and anomaly detection to evaluate the incoming of the network connections. The techniques that use the hybrid-based IDS using machine learning techniques are combined NN and SOM [67], combining ANN, SVM, and MARS [68], etc. Figure 6 shows the basic architecture of an intrusion detection system.

4 Hybrid Intrusion Detection Systems Hybrid-based intrusion detection systems were enormously applied as they produce a better result. The authors concluded that the hybrid-based approaches can overcome the one approach over the other [69]. Neha and Shailendra [70] conclude that hybridbased IDS are relevant methods to get good accuracy and proposed, intelligent water drops (IWD) algorithm described by Shah–Hosseini [71] based feature selection approach. This strategy applies the IWD algorithm, an optimization algorithm that is inspired by the nature of the selection of subset features, combined with SVM as a classifier to evaluate the chosen features. The search of the subset is performed by the IWD algorithm while the evaluation task of the subset is carried by the classifier. Overall this approach reduces the total of 45 features to 10 features from the input dataset. The proposed approach achieved a detection rate, precision, and accuracy of 99% on the KDD Cup-‘99 dataset. Arif et al. [72] introduced a Hybrid approach for intrusion detection systems where PSO is applied to prune the node while pruned decision trees are applied as a classification technique for NIDS. Particle Swarm

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Optimization is a population-based stochastic optimization technique based on bird flocking and fish schooling. The PSO algorithm works by having the population called the swarm and the potential solutions called particles. PSO performs the searching through a swarm of particles that updates through repetitions. Each particle advances in the direction to its previous best position and globally best position for finding the most promising solution. The above approach applies the single and multi-purpose particle swarm optimization algorithm. The evaluation is done on the KDD Cup-‘99 dataset. Thirty arbitrary samples of the datasets are preferred from 10% of KDD Cup-‘99 training and testing datasets. The estimate of records in individual training, as well as testing datasets, is 24,000 and 12,000 jointly for the evaluation purposes. A precision of 99.98% and an accuracy of 96.65% is achieved using the above approaches. Ahmed and Fatma [73] applied a triple edge strategy to develop a Hybrid Intrusion detection system in which the Naïve Bayes feature selection (NBFS) technique has been applied for dimensionality reduction, Optimized Support Vector Machines (OSVM) is applied for outlier rejection and Prioritized K-Nearest Neighbours (PKNN) classifier is employed for classifying the input attacks. Different datasets are applied such as the KDD Cup-‘99 dataset, NSL-KDD dataset, and Kyoto 2006 + dataset. 18 effective features are selected from the KDD Cup-‘99 dataset having a detection rate of 90.28%. 24 features are selected from Kyoto 2006 + dataset with a detection ratio of 91.60%. The author has compared with previous work and has a best overall detection ratio of 94.6%. Tirtharaj Dash et al. [74] report two new hybrid intrusion detection methods that are gravitational search (GS) and sequence of gravitational search and particle swarm optimization called (GSPSO). Gravitational search is a population-based heuristic algorithm that is based on mass interactions and the law of gravity. It comprises of search agent who interacts among each other with heavier masses by the gravitational force and its performance is evaluated by its mass. The slow movement of the search agent assures the good solution of the algorithm. The combinational approach has been carried out to train ANN with models such as GS-ANN and GSPSO-ANN. The random selection of 10% features is selected for training purposes, while 15% is used for testing purposes is applied successfully for intrusion detection purposes. The KDD Cup-‘99 dataset was applied as a metric for calculation. Normalisation of dataset was done for uniform distribution by MATLAB. An average detection ratio of 95.26% was achieved. Yao and Wang [75] introduced a hybrid framework for IDS, K-Means algorithm is employed for clustering purposes. In the classification phase, many machine learning algorithms (SVM, ANN, DT, and RF) which are all supervised learning algorithms are compared on different parameters. The supervised learning algorithm has various parameters for different kinds of attacks (DoS, U2R, Probe, and R2L). The proposed hybrid algorithm has achieved an accuracy rate reaching 96.70% on the KDD Cup-‘99 dataset. Alauthaman et al. [76] proposed a technique of peer to peer, bot detection based on an adaptive multilayer feed forward neural network in cooperation with decision trees. CART is used as a feature selection technique to prefer the relevant features. With all those features, a multilayer feed-forward NN training model is built applying the resilient back-propagation learning algorithm. Network traffic reduction

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techniques were applied by using six rules to pick the most relevant features. Twentynine features are selected from six rules. The proposed approach has an accuracy rate of 99.20% and a detection rate of 99.08% respectively on ISOT and ISCX datasets. Sidra and Faheel [77] introduce a genetic algorithm, which is based on vectors. In this technique vector chromosomes are applied. The novelty of this algorithm is to show the chromosomes as a vector and training data as metrics. It grants multiple pathways to have the fitness function. Three feature selection techniques are chosen, linear correlation-based feature (LCFS), modified mutual information-based feature selection (MMIFS), and forward feature selection algorithm (FFSA). The novel algorithm is tested in two datasets (KDD Cup-‘99 and CDU-13). Performance metrics show that vector-based genetic algorithm has a high DR of 99.8% and a low FPR of 0.17%. Saud and Fadl [78] introduced an IDS model applying the machine learning algorithm to a big data environment. This paper employs a Spark-Chi-SVM model. ChisqSelector is applied as a feature selection method and constructed an IDS model by applying the SVM as a classifier. The comparison is done with Spark Chi-SVM classifier and Chi- Logistic regression classifier. KDD Cup-‘99 dataset is applied for the metrics evaluation process. The result shows Spark Chi-SVM model has good performance having an AUROC of 99.55% and AUPR of 96.24%. Venkataraman and Selvaraj [79] report the hybrid feature selection structure for the efficient classification of data. The symmetrical uncertainty is employed to find the finest features for classification. The Genetic algorithm is applied to find the finest subset of the features with greater accuracy. The author combined (SU-GA) as a hybrid feature selector. Matlab and Weka tools are applied to develop the proposed hybrid feature selector. Different classification algorithm (J48, NB, SMO, DT, JRIP, Kstar, Rand Frst, Multi Perptn,) are used to classify different attacks. The average learning accuracy with Multi Perpn and SU-GA is highest having 83.83% on the UCI dataset. Neeraj et al. [80] applied knowledge computational intelligence in network intrusion detection systems. This paper introduces an intelligent and Hybrid NIDS model. This model, then integrates the fuzzy logic controller, multilayer perception, adaptive Neuro-Fuzzy interference system, and a Neuro fuzzy genetic. The proposed system has trio modules collector, analyser, and predictor modules for gather and filter network traffic, classifying the data, and to prepare the final decision in assuming knowledge on the accurate attack. The experiment is performed on the KDD Cup-‘99 dataset and achieved a false alarm detection accuracy of 99%. Akash and Khushboo [81] implemented a hybrid technique. DBSCAN is applied for choosing the enhanced features for IDS and eliminating the noise from data. Based on Euclidean distance and minimum number of points DBSCAN groups together the points that are close to each other. For grouping data, K means clustering is proposed. SMO (sequential minimal optimization) classifier is implemented for classification purposes. The experimental setup is performed on the KDD Cup-‘99 dataset with certain attributes. The approach DBKSMO achieved an accuracy of about 98.1%. MATLAB and WEKA tools are applied to execute the full process. Rajesh and Manimegalai [82] introduced an Effective IDS applying flawless feature selection outlier detection and classification. The author employs a feature

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selection technique named intelligent flawless feature selection algorithm (IFLFSA) to select the finest number of features that are effective for identifying the attacks. To identify the outliers from the dataset, an entropy-based weighted outlier detection (EWOD) approach is applied. An intelligent layered technique is employed for efficient classification. The KDD Cup-‘99 dataset is applied for experimental purposes. The comprehensive detection accuracy wraps the detection accuracy on four categories of attacks namely DoS, Probe, U2R, and R2L. The detection accuracy of the suggested system has achieved the rate of 99.45%. Unal et al. [83] implemented a hybrid approach for IDS using machine learning approaches. Naive bayes, KNearest neighbor is used for classification purposes, while random forest and J-48 algorithm are used as a decision tree to train the algorithm on different types of attack. The author proposed a CfsSubsetEval approach and WrapperSubsetEval as the two feature selection techniques. NSL-KDD dataset is used as an evaluation process. The overall accuracy of 99.86% is obtained on all types of attacks. Pragma et al. [84] introduce a hybrid IDS to the hierarchical filtration of anomalies (HFA). This work proposes an ID3, a decision tree, which is employed for classifying data into corresponding classes. K-NN is used to assign the class label to an unknown data point that is based on its class labels of the k nearest neighbors. Isolation forest is introduced to isolate an anomaly against normal instances. The proposed algorithm (HFA) is performed on the NSL-KDD dataset and KDD Cup-‘99 dataset. The overall performance on the KDD Cup-‘99 dataset, has an accuracy rate of 96.92%, detection ratio of 97.20% and FPR of 7.49%. The proposed algorithm performance with the NSL-KDD dataset has an accuracy rate of 93.95% has a detection ratio of 95.5%, and an FPR of 10.34%. Sushruta and Chandrakanta [85] report a new approach named BFS-NB hybrid model in IDS. This paper proposes the best first search technique for dimensionality reduction and was employed for an attribute selection technique. The Naïve Bayes classifier is implemented as a classification technique and to improve the accuracy of identifying the intrusions. The BFS-NB algorithm is performed on the KDD dataset gathered from the US air force. The classification accuracy of the BS-NFB is 92.12% while sensitivity and specificity analysis of 97 and 97.5% is achieved. Table 1 depicts the Taxonomy of the latest hybrid intrusion detection methods.

5 Evaluations of Intrusion Detection System This section describes the features of KDD Cup-‘99, NSL-KDD, and Kyoto 2006 + datasets and different metrics to classify the performance of the IDS.

Arif Jamal Malik, Farrukh Aslam Particle swarm Khan: A hybrid technique using optimization (PSO) binary particle swarm optimization and decision tree pruning for network intrusion detection. Cluster Comput Springer 2017. pp: 667–680. DOI 10.1007/s10586-017-0971-8

Ahmed I. Saleh, FatmaM. Talaat, LabibM. Labib: A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM Classifiers. Artif Intell Springer 2017. pp: 403–443. DOI 10.1007/s10 462-017-9567-1

2017

2017

Naïve based feature selection, Optimized SVM Algorithm, Prioritized KNN Algorithm

IWD, SVM

Neha Acharya, Shailendra Singh: An IWD-based feature selection method for intrusion detection system. Soft Computing Springer 2017. pp: 4407–4416. DOI 10.1007/s00500-017-2635-2

2017

Algorithms

Authors/research papers

Year

Table 1 Taxonomy of latest hybrid intrusion detection methods

Hybrid HIDS strategy (Based on Naïve based feature selection), Naïve based is used as feature selection, OSVM is used for outlier rejection. PKNN is used for classifying input attacks

Particle swarm Optimization (PSO) algorithm/PSO are used for node pruning of Decision Trees and the pruned Decision Trees is used for classification of the network intrusions

Feature reduction using IWD (Intelligent water drops), SVM, is used as a classifier

Techniques

KDD Cup-‘99 NSL-KDD and Kyoto 2006 + Dataset

KDD Cup-‘99 Dataset

KDD Cup-‘99 Dataset

Dataset

Feature selection

Detection Rate, Sensitivity, Specificity, Precision

Precision rate, Accuracy rate, IDR, FPR. Time

Yes

Yes

Precision rate, Yes Detection rate, False Alarm rate, Accuracy rate

Evaluation criteria

(continued)

An overall detection rate of 94.6%, Sensitivity rate of 53.24%, Specificity of 98.21%, Precision of 56.62%, is achieved on all the datasets

A precision of 99.98%, Accuracy of 96.65%, IDR of 92.71%, FPR of 0.136 and Evaluation performance of 383.58 sec. is achieved

Achieves a precision rate of 99.10%, Detection rate of 99.40%, Accuracy rate of 99.05%, False alarm of 1.40%

Results

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Authors/research papers

Tirtharaj Dash: A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Comput Springer 2017. pp: 2687–2700. DOI 10.1007/s00500-015-1967-z

Haipeng Yao, Qiyi Wang: An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining. Int J Parallel Prog. Springer 2017. pp: 1-19. DOI 10.1007/s10766-017-0537-7

Mohammad Alauthaman, Nauman Aslam, Li Zhang, Rafe Alasem, M. A. Hossain: A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks. Neural Comput & Applic 29, Springer 2018. pp. 991–1004. DOI 0.1007/s00521-016-25645

Year

2017

2017

2018

Table 1 (continued)

Neural Network with resilient back propagation algorithm, CART

Hybrid Multilevel data mining algorithm

Artificial Neural Network

Algorithms

Dataset

KDD Cup-‘99 Dataset

Feature reduction is done ISOT & by CART, Neural Network ISCX with resilient back dataset. propagation algorithm for updating the weights

MH-DE (Multilevel Hybrid Data Engineering), MH-ML (Multilevel Hybrid Machine Learning, MEM (Micro Expert Module) to train on KDD Cup-‘99 dataset

Gravitational search (GS), NSL-KDD and combination of GS Dataset and particle Swarm optimization (GSPSO) are implemented to train ANN

Techniques

Feature selection

Accuracy rate, Detection rate, FPR

Accuracy rate, Detection rate, Precision rate, Recall, F-Value

Yes

Yes

Detection rate, No Mean Squared Error MSE, Time

Evaluation criteria

(continued)

Detection of 99.08%, An accuracy rate of 99.20%, and FPR of 0.75% is achieved

Accuracy of 96.70%, Precision of 96.55%, F-Value of 96.60%, Recall of 96.70% and on all types of attack a detection rate of 66.69% is achieved

A detection ratio of 95.26%, MSE of 0.4527% and training time of 103.70 seconds is achieved

Results

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Sivakumar Venkataraman, Rajalakshmi Selvaraj: Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification: Advances in systems, control and application. Springer 2018. pp: 499–514. DOI 10.1007/978-981-10-4762-6_48

2018

Symmetrical Uncertainty and genetic algorithm (SU-GA) is used as classification algorithm

Symmetric uncertainty is used to obtain best features/genetic algorithm is applied on selected features

Chisqselector using SVM classifier for feature reduction

Suad Mohammed, Fadl Mutahe: Intrusion detection model using machine learning algorithm on Big Data environment: In proc. journal of big data. Springer 2018. pp: 1-12. DOI 10.1186/s40537-018-0145-4

2018

Support Vector Machine (SVM)

Sidra Ijaz, Faheel A. Hashmi: Vector based genetic Chromosomes as vector Vector Based Genetic Algorithm algorithm and training data as to optimize predictive analysis in metrics’ network security: Applied intelligence, Volume 48, issue 5, Springer 2018. pp: 1086-1096. DOI 10.1007/s10489-017-1026-9

Techniques

2018

Algorithms

Authors/research papers

Year

Table 1 (continued)

AUROC, AUPR, Time

Accuracy rate, FPR

Evaluation criteria

UCI dataset. Accuracy rate

KDD Cup-‘99 dataset

CTU-13 dataset and KDD Cup-‘99 dataset

Dataset

Yes

Yes

Yes

Feature selection

(continued)

The average learning accuracy of 83.83% is achieved, and overall evaluation time of 0.23 seconds is achieved on all methods

Area under curve (AUROC) of 99.55%, Area under precision recall curve (AUPR) of 96.24% and training time of 10.79 se

Accuracy rate for DOS is 99.8% and FPR of 0.17% is achieved

Results

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Akash Saxena, Khushboo Saxena: DBSCAN, SMO, KHybrid Technique Based on Means clustering DBSCAN for Selection of Improved Features for Intrusion Detection System: Emerging Trends in Expert Applications and Security. pp: 365377. Springer 2019. DOI 10.1007/978-981-13-2285-3_43

2019

Genetic algorithm

Neeraj Kumar, Upendra Kumar: Knowledge Computational Intelligence in Network Intrusion Detection Systems. In: Knowledge Computing and Its Applications. Springer 2018. pp: 161-176. DOI 10.1007/978-981-10-6680-1_8

2018

Algorithms

Authors/research papers

Year

Table 1 (continued) Dataset

DBSCAN is used to eliminate noise in data, K-Means is used for grouping of data, and SMO is used for detection of intrusions.

KDD Cup-‘99 dataset.

Fuzzy Logic Controller, KDD Neuro- fuzzy inference Cup-‘99 system, Multi layer dataset perception, Neural Fuzzy genetic for classification of attacks

Techniques

Accuracy rate, Detection rate

Accuracy rate

Evaluation criteria

Yes

Yes

Feature selection

(continued)

An approx accuracy of 98.1% and approx Detection rate of 70% is achieved

A detection accuracy of 99% is achieved

Results

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Authors/research papers

Rajesh Kambattan, Manimegalai Rajkumar: An Effective Intrusion Detection System Using Flawless Feature Selection, Outlier Detection and Classification: Progress in Advanced Computing and Intelligent Engineering. Springer 2019. pp: 203–213. DOI 10.1007/978-981-13-1708-8_19

Unal Cavusoglu: A new hybrid approach for intrusion detection using machine learning methods: Applied Intelligence (2019) 49. pp: 2735–2761. Springer 2019. DOI 10.1007/s10489-018-01408-x

Year

2019

2019

Table 1 (continued)

Naive bayes, Random forest, J-48, K-Nearest Neighbor (KNN) algorithm

Intelligent flawless feature selection algorithm (IFLFSA), Entropy Based weighted outlier rejection (EWOD), Intelligent layered classification algorithm

Algorithms

Dataset

CfsSubsetEval and WrapperSubsetEval as a two feature selection techniques, while Naive bayes, Random forest, J-48, K-Nearest Neighbor (KNN) algorithm is used as a classifier

NSL-KDD Dataset

IFLFSA is used as feature KDD selection; EWOD is used Cup-‘99 to detect outliers in data. dataset Intelligent layered classification algorithm is applied to classify the data

Techniques

Accuracy rate, Detection Rate, FP rate, F–measure, MCC, TP rate, Time

Accuracy rate

Evaluation criteria

Yes

Yes

Feature selection

(continued)

Overall detection ratio of 0.9828%, Overall accuracy rate of 99.86%, Overall TPR of 0.929%, Overall FPR of 0.00042%, Overall F-Measure of 0.9625 %, MCC of 0.955 % and total time to evaluate is 10.625 seconds (Performed on NSL KDD, 25 attributes on all types of attacks)

Overall accuracy of 99.45% is achieved

Results

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Sushruta Mishra, Chandrakanta Mahanty, Shreela Dash, Brojo Kishore Mishra.: Implementation of BFS-NB Hybrid Model in Intrusion Detection System: Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer. 2019 pp: 167–175. DOI 10.1007/978-981-13-1280-9_17

2019

Best first search and Naïve bayes, (BFS-NB) algorithm.

Pragma Kar, Soumaya Banerjee, ID3, K-Nearest Kartick Chandra Mondal, Gautam neighbor, Isolation Mahapatra, Samiran Forest, Chattopadhay: A Hybrid Intrusion Detection System for Hierarchical Filtration of Anomalies. In: Information and Communication Technology for Intelligent Systems. Smart Innovation, Systems and Technologies, vol 106. Springer 2019. pp: 417-426. DOI 10.1007/

2019

Algorithms

Authors/research papers

Year

Table 1 (continued)

KDD Cup-‘99 dataset, NSL-KDD Dataset

Dataset Accuracy rate, Detection rate, False alarm rate

Evaluation criteria

Naïve bayes is used as KDD Accuracy, classifier and best search is dataset from Specificity, used as attribute US Air force Sensitivity optimization method.

ID3 is applied as feature selector, K-nearest neighbor is used to apply a class to unknown data point, Isolation forest is applied to segregate normal data from anomaly

Techniques

Yes

Yes

Feature selection

The classification accuracy of BS-NFB is 92.12%, Sensitivity analysis of 97% and specificity of 97.5% is achieved

Performance on NSL-KDD dataset has accuracy of 93.95% has a detection rate of 95.5% and a false alarm rate of 10.34%.The performance with KDD Cup-‘99 dataset has an accuracy of 96.92%, detection rate of 97.20% and false alarm rate of 7.49%

Results

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5.1 KDD Cup-‘99 Dataset The KDD Cup-‘99 is an extensively used dataset in the field IDS. KDD Cup-‘99 data is captured from the DARPA 98, IDS evaluation program. The entire KDD Cup-‘99 dataset comprises 4,898,431 single connection records; each contains 41 features classified as normal or attacks. KDD Cup-‘99 dataset classifies the attacks into four types, namely Probe, User to Root, Denial of Service, and Remote to Local [86]. • Probe. The intruder gathers the knowledge from the networks or hosts scans the whole networks or hosts that are prone to attacks. An intruder then exploits the system vulnerabilities by looking at the known security breaches so that the whole system is compromised for malicious purposes. • Denial of Service (DOS). This kind of attack results in the unavailability of computing resources to legitimate users. The intruder overloads the resources, by making the computing resources busy, so that genuine users are unable to utilize the full resources of the computer. • User to Root (U2R). The intruder tries to acquire the root access of the system or the administrator privileges by sniffing the passwords. The attacker then looks for the vulnerabilities in the system, to acquire the gain of the administrator authorization. • Root to Local (R2L). The intruder attempts to gain access to the remote machine, which does not have the necessary and legal privilege to access that machine. The attacker then exploits the susceptibility of the remote system, tries gaining access rights to the remote machine. Table 2 describes the categories of attack and attack names. The KDD Cup-‘99 intrusion datasets mainly consist of three components: The entire KDD Cup-‘99 dataset consists of the examples of normal and attack connections, 10% KDD dataset consists of the data, which aims for training the classifiers, and the KDD test dataset for the testing of classifiers. Table 3 describes the dataset features. The connection features can be classified into four categories namely: • Basic features. It is accessed from the header of the packet, without inspecting the packet contents, such as (protocol type, duration, flag, service, and the number of bytes that are sent from the source to the destination and contrarily). Table 2 Four categories of attack Category of attack

Attack name

Probe

satan, portsweep, nmap, ipsweep

DoS

neptune, back, smurf, land, teardrop, pod

U2R (User to Root)

perl, loadmodule, buffer_overflow, rootkit

R2L (Remote to Local)

warezlient, spy, warezmaster, multihop, guesspasswd, phf, imap, ftp_write

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Table 3 KDD Cup-‘99 Dataset Features Index Feature name

Description

1

Duration

Connection length

2

protocol_type

Protocol types (TCP, ICMP, and UDP)

3

Service

Mapping of destination port to the service (ftp, telnet, http…)

4

Flag

Connection status

5

src_bytes

Source to destination. (Total number of bytes)

6

dst_bytes

Destination to source. (Total number of bytes)

7

Land

If the address of the source and destination are same from the place, then it is = 1/otherwise 0

8

wrong_fragment

Indicates the number of the wrong fragments

9

Urgent

Indicates the number of the urgent packets

10

Hot

Indicates the number of the hot indicators

11

num_failed_logins

No. of failed attempts at login

12

logged_in

If logged in = 1/if login failed = 0

13

num_compromised

It indicates the compromised states

14

root_shell

With a root account a command interpreter is running then root shell = 1/otherwise its 0

15

su_attempted

su is attempted = 1/otherwise its 0

16

num_root

Number of times the root is accessed

17

num_file_creations

To create new files, the number of operations used

18

num_shells

Number of command interpreters that are active

19

num_access_files

Number of operations to create the file

20

num_outbound_cmds

In the ftp sessions the number of outgoing commands

21

is_host_login

If the login is on the host login file list then, is host login = 1 /otherwise it is 0

22

is_guest_login

If a guest logs into system then, is guest login = 1/ otherwise it is 0

23

Count

At a given interval, the number of connections to the same host as the current connection

24

srv_count

At a given interval, the number of connections to the same service as the current connection

25

serror_rate

SYN errors with proportion of connections

26

srv_serror_rate

SYN errors with proportion of connections

27

rerror_rate

REJ errors with proportion of connections

28

srv_rerror_rate

REJ errors with proportion of connections

29

same_srv_rate

Same services with proportion of connections

30

diff_srv_rate

Different services with proportion of connections

31

srv_diff_host_rate

Different hosts with proportion of connections (continued)

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Table 3 (continued) Index Feature name

Description

32

dst_host_count

Same destination having the number of connections

33

dst_host_srv_count

Same destination that apply the same service having the number of connections

34

dst_host_same_srv_rate

Same destination that apply the same service having the number of connections

35

dst_host_diff_srv_rate

Different services having same host, has the number of connections

36

dst_host_same_src_port_rate System having same source port has the number of connections

37

dst_host_srv_diff_host_rate

38

dst_host_serror_rate

Host having a S0 error has the number of connections

39

dst_host_srv_serror_rate

The host and the specified service having S0 error have the number of connections

40

dst_host_rerror_rate

The host having an RST error has the number of connections

41

dst_host_srv_rerror_rate

The host and the specified service having an RST error have the number of connections

Same service incoming from different hosts having the number of connections

• Content features. It is decided by investigating the details of the TCP packet (number of failed attempts to login the computer). • Traffic features. The features are calculated in proportionate to window intervals. It is further divided into the same service features and same host features. The same service features it analysis the connections in last two seconds having the same service as a present connection. The same host features, it analyses the connections in the last two seconds of the host having the same destination as its present connection and then computes the stats of the protocol behavior. • Time features. The same service features and same host features and are also called time-based features. The time features decide the length of time from the source IP address to the destination IP address.

5.2 NSL-KDD Dataset KDD Cup-‘99 datasets are having several redundant records and duplicate records. The redundant record is having an overall 78% and duplicate records have an overall 75%. This redundancy and duplicity prevent it from classifying the other records [87]. A contemporary NSL-KDD dataset was proposed [88] that does not include the redundant and duplicate records in training and testing data [89], which helped in overcoming the redundancy and duplicity issue. There is a total 37 attacks in

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Table 4 NSL-KDD: Number of instances in training and test data Attack Classes

Total number of instances in the training Total number of instances in the test set set

DoS

land (18), teardrop (892), pod (201), neptune (41,214), smurf (2,646), back (956)

neptune (4,657), pod (41), smurf (665), apache2 (737), worm (2), mailbomb (293), processtable (685), udpstorm (2), teardrop (12), land (7), back (359)

Probe

portsweep (2,931), ipsweep (3,599), nmap (1,493), satan (3,633),

satan (735), ipsweep (141), nmap(73), portsweep (157), mscan (996), saint (319)

R2L

guess_passwd (53), ftp_write (8), imap (11), phf (4), multihop (7), warezmaster (20), warezclient(890), spy(2)

ftp_write (3), phf (2), warezmaster (944), snmpguess (331), httptunnel (133), named (17), guess_passwd (1,231), imap (1), multihop (18), xlock (9), snmpgetattack (178), xsnoop (4), sendmail (14)

U2R

buffer_overflow (30), rootkit (10), perl (3), loadmodule (9)

rootkit (13), loadmodule (2), perl (2), sqlattack (2), xterm (13), ps (15), buffer_overflow(20)

Total

58,630

12,833

the testing dataset out of which 21 different attacks are of training dataset and the remaining attacks are available only for testing the data. The attack classes are categorized into DoS, Probe, R2L, and U2R categories. In the training data, the normal traffic consists of 67,343 instances having an overall of 125,973 instances. In test data the normal traffic consists of 9711 instances, having an overall of 22,544 instances. Table 4 depicts the total stats of instances in training and test data.

5.3 Kyoto 2006 + Dataset The Kyoto 2006 + dataset consists of original data that is captured from network traffic data between the years 2006 and 2009. The new version consists of further data collected from the year 2006 to 2015. This dataset is taken by applying the email server, honey pots, dark net sensors, and WebCrawler [90, 91]. The authors have employed various types of darknet, honeypots on five of the networks in indoors and outdoors of the Kyoto University and from the honeypots, the traffic data is possessed. There are 50,033,015 normal session, 43,043,225 attack sessions and 425,719 sessions relevant to unknown attack. The author’s extracted 14 statistical features from the honeypots of 41 features from the KDD Cup-‘99 dataset. Table 5 shows the statistical features of the Kyoto 2006 + dataset, which is taken from the dataset of KDD Cup-‘99. Besides, the authors also obtained ten features extra, which facilitate the authors to further examine, what kind of attacks appear on computer networks. The redundant

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Table 5 Statistical features in the Kyoto 2006 + dataset derived from the KDD Cup-‘99 dataset Index Feature name

Description

1

Duration

Connection length (in number of seconds)

2

Service

Connection service types such as (http, telnet, etc.)

3

Source bytes

The source IP address that sends the number of data bytes

4

Destination bytes

The destination IP address that sends the number of data bytes

5

Count

For past two seconds, the number of connections having same for Source and destination IP address for current connection

6

Same_srv_rate

Identical service having the count feature with proportion of connections

7

Serror_rate

‘SYN’ errors having the count feature with proportion of connections

8

Srv_serror_rate

‘SYN’ errors having the service count with proportion of connections

9

Dst_host_count

Between the past 100 connections whose destination IP address is same to the current connection, the number of connections having source IP address is same to that of the current connection

10

Dst_host_srv_count

Between the past 100 connections whose destination IP address is same to that of the current connection, the number of connections having service type is same to that of the current connection

11

Dst_host_same_src_port_rate Source port having the same of the current connection in Dst_host_count feature with proportion of connections

12

Dst_host_serror_rate

‘SYN’ errors having Dst_host_count feature with proportion of connections

13

Dst_host_srv_serror_rate

‘SYN’ errors having Dst_host_srv_count feature with proportion of connections

14

Flag

The connections state at the time of which connection was written

and insignificant features, content features are ignored as they are unsuitable and time consuming for NIDS to obtain without domain knowledge. Table 6 depicts the additional features of the Kyoto 2006 + dataset.

5.4 Performance Metrics This section describes various performance metrics used to validate the results in IDS. Those metrics indicate all the measurements that are used by researchers to

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Table 6 Additional features in Kyoto 2006 + dataset Index

Feature name

Description

1

IDS_detection

It indicates that if IDS is started an alarm for the connection. ‘0’ indicates that alarms are not triggered and an Arabic numeral indicates various kinds of alarms. Parenthesis implies the same amount of alarm

2

Malware_detection

It specifies that if malware was noticed at the connection. ‘0’ indicates that malware was not noticed, and string implies the identical malware noticed at the connection. Parenthesis implies the amount of similar malware

3

Ashula_detection

It indicates if exploit codes and shellcode were applied in connection. ‘0’ indicates no exploit code and shellcode were observed, and an Arabic numeral implies the distinctive types of the exploit codes or shellcodes. Parentheses indicate the same figure of exploit code or shellcodes

4

Label

It means that, there was attack in the session or not ‘1’ indicates as normal. ‘−1’ means familiar attack was observed in the session, and ‘−2’ indicates unidentified attack was observed in the session

5

Source_IP_Address

It indicates that source IP address is utilized in session

6

Source_Port_Number

It implies that the source port number is utilized in the session

7

Destination_IP_Address

It implies that the destination IP address is utilized in the session

8

Destination_Port_Number

It implies that the destination port number is utilized in the session

9

Start_Time

It implies that at which moment the session was started

10

Duration

It indicates the total time duration when the session is set

prove the results obtained in any of: True positive (TP), True negative (TN), False negative (FN), False positive (FP) [92].

5.4.1

Confusion Matrix

It is known as error metrics, allowing the correlation between predicted and actual classes. It is important for measuring the accuracy, recall, precision, specificity, and ROC, AUC curve. It helps in the visualisation performance of the algorithms and it is often applied to depict the performance of the classifier on the testing dataset. Class

Predicted positive class

Predicted negative class

Actual positive class

FN

TP

Actual negative class

TN

FP

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Accuracy

The ratio of true predictions, precisely classified instances, calculated by: Accuracy =

5.4.3

TN + TP (TN + TP + FN + FP)

Error Rate

The ratio of all the predictions made, which are maliciously classified: It is calculated by: ER = 1 − Acc.

5.4.4

True Positive Rate

It is termed as the intrusions that are accurately classified as an attack by the IDS. It is also called recall, sensitivity, or detection rate. It is calculated by: TPR =

5.4.5

TP FN + TP

False Positive Rate

It is the regular patterns that are wrongly identified as an attack. It is also called a false alarm rate is given by: FPR =

5.4.6

FP TN + FP

True Negative Rate

It is the regular patterns that are correctly identified as normal. It is also known as specificity given by: TNR = 1 − FPR.

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5.4.7

117

False Negative Rate

It is termed as the intrusions that are wrongly identified as normal. It is calculated by: FNR = 1 − TPR.

5.4.8

Precision

It is the amount of behavior precisely classified as an attack. It is calculated by: Precision =

5.4.9

TP (FP + TP)

F-measure

It is the measurement of accuracy tests. It is described as the weighted harmonic mean of the precision and recall and it also called as f-value or f-score: FM = 2 ×

5.4.10

recall × precision recall + precision

Matthews’s Correlation Coefficient

It is used only in the binary intrusion detection system in which it computes the observed and predicated binary classification. It is calculated by: (TP × TN) − (FP × FN) MCC = √ (TP + FN)(TP + FP)(TN + FN)(TN + FP) The detection achievement of IDS is classified using the area under curve (AUC) and receiver operating characteristic (ROC). ROC reveals the changes in detection ratio with contrast in the internal verge to produce a low or high false positive rate. If AUC values are larger, then the classifier performance will be better. Another important metric that are considered in the evaluation of IDS is CPU consumption, its throughput, and power consumption which may run on different hardware configuration on high speed networks with or without the limited number of the resources.

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6 Research Opportunities The inception of Intrusion Detection Systems has started in the year 1980, numerous literature studies have presented on this topic to date, there exists a vide varieties of research opportunities in this field. Some of the research opportunities are listed below. • Trusted IDS. There is a demand to develop a dependable intrusion detection systems that accurately classifies the novel intrusions, such a degree that it can be efficiently trusted without or little human interventions as possible. The robustness of IDS still needs further investigation to new evasion techniques. • Nature of Attacks. The knowledge information about the nature of new intrusion attacks needs to be continuously studied and updated frequently, as the attackers are motivated enough and try different ways to intrude the IDS systems. Updating the knowledge of new attacks helps in preventing the new attacks that penetrate the IDS systems. • The efficiency of IDS. One of the major concerns about IDS that exists today is its run time efficiency. There is a concern about IDS, that it consumes more resources results in degrading the IDS performance. The need of the hour is to develop efficient IDS that is computationally inexpensive and consumes fewer resources without affecting the performance factors of IDS. • Datasets. NSL-KDD, Kyoto 2006 +, and KDD Cup-‘99 are considered as the benchmark datasets in the field of IDS, still one needs to evaluate the performance of IDS on other datasets such as CAIDA, ISCX, ADFA-WD, ADFA-LD, CICIDS, BoT-IoT, etc. Evaluating the performance of IDS in different datasets helps us to know the actual performance of IDS on different environments.

7 Conclusion In this study, an analysis of various intrusion detection systems that applies the various machine learning techniques was observed. Different hybrid machine learning-based intrusion detection techniques, proposed by various researchers have been analyzed. The outcome of the results and observing various parameters such as accuracy, detection rate, and false positive alarm rates are the most important contributions of this study. The study of the KDD Cup-‘99, NSL-KDD, and Kyoto 2006 + dataset, with its different parameters, are discussed in this article. Finally, observation of the various performance metrics used for evaluation of results in intrusion detection systems is analyzed. By observing various parameters in this article it is concluded that “A Network Hybrid Model for Intrusion Detection System” was an efficient one. Acknowledgements Sincere thanks to Dr. Philippe Fournier-Viger, the founder of the SPMF data mining library for providing us his deep insights into our work.

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Indexing in Big Data Mining and Analytics Ali Usman Abdullahi , Rohiza Ahmad, and Nordin M. Zakaria

Abstract Big data analytics is one of the best ways of extracting values and benefits from the hugely accumulated data. The rate at which the global data is accumulating and the rapid and continuous interconnecting of people and devices is overwhelming. This further poses additional challenge to finding even faster techniques of analyzing and mining the big data despite the emergence of specific big data tools. Indexing and indexing data structures have played an important role in providing faster and improved ways of achieving data processing, mining and retrieval in relational database management systems. In doing so, index has aided in data mining by taking less time to process and retrieve data. The indexing techniques and data structures have the potential of bringing the same benefits to big data analytics if properly integrated into the big data analytical platforms. A lot of researches have been conducted in that direction, and this paper attempts to bring forward how the indexing techniques have been used to benefit the big data mining and analytics. Hence, this can bring the impact that indexing has on RDBMS to the folds of big data mining and analytics. Keywords Big data · Big data analytics · Data mining · Indexing techniques · Index

A. U. Abdullahi (B) Computer Science Education Department, Federal College of Education (Tech), Gombe, Nigeria e-mail: [email protected] URL: http://www.fcetgombe.edu.ng R. Ahmad · N. M. Zakaria Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia e-mail: [email protected] N. M. Zakaria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_5

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1 Introduction The exponential growing nature of global data was collated and presented by the Statista in their report titled—Volume of data/information created worldwide from 2010 to 2025. The report indicated that the world’s overall volume of both created and copied data as of that 2020 year would be 50.5ZB. Also, this volume is expected to rise three-fold within five years and is estimated to be 175 by 2025 [38]. This fact gives a sense of hugeness of data size that is termed as big data [9, 10]. This global data is accumulated from various forms, comprising structured, semi-structured and unstructured masses of data that need analysis so dearly. Since the data are collated and stored into datasets, then those enormous datasets are also referred to as big data/datasets [5, 34, 44]. According to Chen et al. [9, 10], the increase in volume indicates how big the generation, the collection and the scaling of data masses have become. While increase in velocity indicates the need for timely and rapid collection and analysis of big data in order to maximally utilize its commercial value, the increase in variety indicates that big data comprises various forms which may include unstructured and semistructured, besides the usual structured data [4]. The IDC on its part presented a different opinion on big data. In its 2011 report, IDC viewed big data from its technological and architectural angle as the data is designed for extraction of economic value. The economic value comes from the very large volume and widely varied data, which have high velocity of discovery, capture and analysis [44]. The IDC definition added another ‘V’ to make it 4Vs model, with the fourth ‘V’ being value. Therefore, a broader definition of the big data could be derived from all earlier ones as the term is used in describing enormous datasets, whose contents are characterized by the 4Vs: (i) Volume—very large amount of data; (ii) Variety—different forms of the data, i.e., structured, semi-structured and unstructured gathered from different sources including images, documents and complex record; (iii) Velocity—the data has been constantly changing contents, which come from complementary data collection, archived and streamed data [6, 10, 45]; and (iv) Value—very huge value and very low density [10]. In addition, recent literatures include veracity as the fifth ‘V’, the characteristic of the big data after being convinced that none of the earlier described characteristics of big data have covered that. By veracity, it means that there are uncertainty and effect of accuracy on the quality of collected data [4, 5, 42, 44]. The value in big data is extracted by proper and efficient retrieval of the data from the big datasets. Thus, fast processing of the retrieved data is determinant to faster and timely analysis and access to the required data. Indexing is one of the most useful techniques for faster data retrieval during processing and accessing. Therefore, the industrial 4.0 data-driven processes are in need of such faster data retrieval. Once the retrieval and access processes involving big data usage are made faster, a lot of benefits are achieved. These benefits include energy/power saving and improving hardware durability and reduce heat generation.

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1.1 Objective of the Chapter The main objective of this chapter was to present indexing techniques that can be used in searching and effective retrieval of data during data mining. The strategy of all the indexing techniques is to restrict the amount of input data to be processed during the mining of any dataset. The chapter highlights those indexing techniques that have the potentials of working better with big data.

1.2 Taxonomy of the Chapter The focus of the chapter is to include in it all relevant literatures that are searched and downloaded from the major publication databases. The databases include IEEEXplore, DBLP, Scopus, Springer and others. Then, titles, abstracts, introductions and conclusions of papers that covered application of indexing in big data mining and analytics were described. This was done using selection criteria in order to pick the papers that matched and have highlighted the structure of various indexing approaches used for big data mining and analytics. In addition, the chapter identifies the indexing approaches that have potential for big data mining and those that have less potential. Figure 1 depicts the diagrammatic sketch of the taxonomy used for the chapter. The chapter’s perspective is to center the discussion of literatures on the indexing approaches, their data structure, their mode of application, their pros and cons, and prospect for big data mining. In addition, the chapter attempts to cover indexing from its application on RDBMS up to it’s use in big data mining and analytics. This may enable specialist and practitioners of big data mining and analytics to have wider view on the indexing approaches, and when, how and where to apply each of the approaches for better results.

Fig. 1 A sketch of the taxonomy used for the chapter

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The remaining part of the chapter is organized as follows. Sect. 2 presents the basic types of the indexing, Sect. 3 discusses the online indexing which is improvement of the basic indexing. Section 4 enumerates the indexes inbuilt to MapReduce process, which is a dedicated process of big data processing. Then, Sect. 5 presents the user-defined indexes, and finally, Sect. 6 concludes the paper.

2 Index and Indexing Indexing, as information retrieval technique, is the process of generating all the suitable data structures that allow for efficient retrieval of stored information [35]. The term index refers to the suitable data structure needed, to allow for the efficient information retrieval [26]. Usually, the data structures used for indexing in most cases do not store the information itself. Rather, it uses other data structures and pointers to locate where the data is actually being stored. A meta index is also used to store additional information about stored data like the external name of a document, its length, which can be ranked as the output of the index. In RDBMS, the index is a data structure that speed up the operation of data retrieval from database tables. The index comes with additional cost for writing/reading it into/from the index table. This leads to more storage usage in order for the extra copy of data created by the index to be maintained. The indexes are used in locating data quickly by going straight to the data location without having to search all the rows in the database table every time that table is accessed. Indexes are the bases for providing rapid random lookups and efficient access of ordered recorded. While in big data, if index is to be implemented the same way as it works with the RDBMS, there is no doubt that it will be so big to the extent that the intended fast record retrieval may not be achieved. Therefore, there is a need for a closer look at the indexing technique in a way that will be reasonably small and maintain the targeted goal of faster record retrieval.

2.1 Index Architecture and Indexing Types The index has two architectures: non-clustered and clustered index, as shown in Fig. 3. The non-clustered architecture presents data in an arbitrary order, but maintains a logical ordering in which rows may be spread out in a file/table without considering the indexed column expression. This architecture uses a tree-based indexing that has a sorted index keys and pointers to record at its leaf node level. The non-clustered index architecture is characterized by having the order of the physical rows of the indexed data differing with the order in the index [32]. The non-clustered index sketch is displayed in Fig. 3 (Fig. 2). On the other hand, clustered index architecture changes the blocks of data into a certain distinct order to match the index. This results in the ordering of the row

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Fig. 2 A sketch of index types

Fig. 3 A sketch of non-clustered index

data. It can greatly increase the overall speed of retrieval in a sequential accessed data or reverse order of the index or among a selected range of items. The major characteristic of the clustered index architecture is that the ordering of the physical data rows is in accordance with that of the index blocks that points to them [29]. The clustered index sketch is displayed in Fig. 4. There are many types of indexing in existence, and below is the overview of some of them, as well as some of the studies involved each of them and their applicability in big data situation.

2.2 Bitmap Index The bitmap index uses bit array called bitmaps to store the bulk of its data. Bitmap is a special type of index that uses bitwise logical operation on the bitmaps to answer most of the queries run against it. The bitmap index works basically in situations where index values are repeated very frequently unlike other index types commonly used. The other types are most efficient when indexed values are not repeated at all or they are repeated smaller number of times. The gender field of a database table is

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Fig. 4 A sketch of clustered index

a good example for a bitmap index. This is so because no matter how many tuples there are in a database table, the field will only have two possible values: male or female. A typical bitmap index data structure is shown in Fig. 5. The bitmap index has been used in wide variety of areas and in big data analytics. For instance, bitmap index application was used in the aspect of index compression. The approach helps the compression to work better with high cardinality attribute data. Wu et al. [39] present a study and analysis of some of the compression techniques that use bitmap indexing, namely, byte-aligned bitmap compression (BBC) and wordaligned hybrid (WAH). These techniques were able to reduce compressed data sizes and improve their performance. The authors’ motivation was the fact that most of the empirical researches do not include comparative analysis among these different techniques, and the result of their own work showed that compressed bitmap indexes appeared to be smaller in size compared to that of B+-Tree, with WAH occupying half of the space of B+-Tree while the BBC occupies half the space of WAH.

Fig. 5 A bitmap index form from gender table

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Another work was done by Fusco et al. [13] using bitmap index as a compression approach to minimize CPU workload and consumption rate of disk. The platform used for the work was a streaming network data, which requires a realtime indexing. The authors introduced what they called COMPAX, a variant of compressed bitmap index that supersedes the word-aligned hybrid (WAH) in terms of throughput of indexing, shorter retrieval time and higher compression rate. The NETwork Flow Index (NET-FLI), which highly optimizes real-time indexing as well as data retrieval from larger-scale repositories of network, was used. The NET-FLI synergies COMPAX and locality-sensitive hashing (LSH) are used for streaming reordering in an online setup to achieve the target of the research. This combination results in higher insertion rates of up to 1 million flows per second many folds over what is obtainable in typical commercial network flow. The ISPB also allows the performance of complex analysis jobs by administrators. In addition, an effort was made to automate indexing process as well as resolving the index selection problem (ISP) using bitmap by [27]. They came up with a technique that merges the features of index selection techniques (IST) and those of linear programming for optimization to minimize cost. The result was a new method that solves ISP externally and uses optimizer for choosing the set of indexers to be used. The clustering data mining technique was used and when benchmarked it outperformed Microsoft SQL Server Index Selection Tool (IST) in terms of speed of selection and suggestion of indexes. Even though the bitmap index was designed for RDBMS, it has also been used on some HLQLs on big data to improve information retrieval. For example, a group of students incorporated bitmap index into Hive to improve the retrieval of Facebook users information using gender field. The bitmap technique as a candidate of indexing in big data has been tried, and in some specific situation it gives some improved results. However, the bitmap index does generate large volume of data as its data structure alongside the volume of the big data itself. Hence, it cannot be used for general data retrieval in big data because big data contains variety of data values.

2.3 Dense Index The dense indexing approach uses a file/table with pair of keys and pointers to each record in the data file/table, which is sorted. Every key in the dense index is associated to a specific pointer to one of such records. If the underlining architecture of the indexes is a clustered one with duplicate keys, dense index just points to the first record with the said key [15]. Figure 6 displayed a sample of the dense index. In the dense index, each index entry consists of a search key and a pointer. The search key holds the value to be searched while the pointer stored the identifier to the disk location containing the corresponding record and the offset that identifies the point where the record starts within the block. The dense index as an ordered index either stores index entry for all records when the approach it is using is non-clustered, or it just stores the index entry to the first

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Fig. 6 A dense index on employee table

search key when using the clustered index approach [35]. The dense index uses file in storing its index data structure and there is a dedicated pointer to each record and the data has to be ordered. These two facts suggested that the index is going to grow so big that it will be close to the size to that of the stored data. Hence, there are no studies using dense index in big data retrieval.

2.4 Sparse Index The sparse indexing method also uses a file/table that contains pair of search keys and pointers. The pointers are pointing to blocks instead of individual records in the data file/table, which is sorted in the order of the search keys. In sparse index, index entries appeared for some of search key values. An index entry is associated to a specific pointer to one of such blocks. If the underlining architecture of the indexes is a clustered one with duplicate key, then the index just points to the lowest search key in each block [15]. To locate any search key’s record, an index entry is searched with the largest value that is less than or equal to the given search key value. Then, the searching starts at the record pointed to by the index entry and moves down until the target is found [32, 35]. Figure 7 depicts the explanation given above for sparse index.

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Fig. 7 A sparse index on employee table

The dense and sparse indexes are the most common types of ordered index that most relational databases use for generating query execution plans. However, for both the dense and sparse index types, the use of files/tables to keep the pairs of search keys and pointers may make them very unsuitable for big data indexing due to two reasons: (1) The records and block of data file for big data will be distributed over different clusters, which will make it very difficult to maintain such files. (2) The volume of the big data will make the size of such index files to be unnecessarily very large, which may lead to unreasonable costs of space and maintenance time.

3 Online Indexes 3.1 Online Indexing Most techniques of indexing are having one drawback or the other. Thus, the big data indexing requires the study of other modified indexing techniques. Some of these modified indexes include the work of Chaudhuri et al. [8], which introduced the online indexing. The online index is an extension to the use of external tuning tools to optimize the physical design of a database through the analysis of representative workload set.

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This technique works by monitoring the steps of an actual workload without knowing it upfront, and to create an index automatically when one of the query’s execution plans is generated. The index is created at the background of a running workload in one complete go. The online indexing has lessened the work on the side of the database administrator and on the side of the system by avoiding the need of creating the index externally. However, the online indexing usage has not covered the multi and partial indexes, which are necessary and important approaches for query processing in both OTP and OLAP. The results of comparison between the online indexing and the conventional indexing showed that the online index performed better than the conventional one due to the following: knowledge of workload is not required before creating the index. Since the index is created as side effect of query execution, its entries cover only what is specified in the query’s predicates; hence, the reason for better performance. The online index can use any of the basic indexing data structure for its implementation, be it B-Tree or ordered tables-based index [8, 16, 21]. Amir et al. [2] used online indexing to solve the problem of managing unbounded length keys that are found in XLM paths, IP addresses, multi-dimensional points, multi-key data and multi-precision numbers. This type of data is categorized as big data. These types of string-based keys are usually atomic and indivisible; hence requires a customized comparison data structure. Their proposed online indexing happens to work with any data structure with a worst-case complexity of O(logn), which they reported to be the best based on their experiment. A suffix tree was cited as one of the application area of the proposed online indexing [2].

3.2 Database Cracking Another effort in the direction of ‘on-the-fly’ indexing was made by Idreos et al. [21]. The authors introduced a combination of automatic index selection and partial indexes called database cracking. This approach uses the side effect of running current query and future ones to refine the structure of its index. The database cracking substituted the normal scanning of stored data for index creation and query processing, with pivoted partitioning of the data using the predicates from the query. By doing that, one of the partitions will be containing only the tuples that answers the query. The underlining data structure used by the cracker index could be quick sort generated B-Tree or a hash, whose partitions expected to be beneficial to arriving queries. In database cracking, no external index or external tuning tools are required, but just the monitoring of queries. The present and arriving queries are used to build and optimize the index. Database cracking proves to be advantageous compared to online indexing, and by extension the ordinary query processing. For the fact that the partitioning has to be carried out in small number for every predicate (fan-out of 2–3), it takes a lot of queries to get the index fully optimized [17, 18, 41].

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Fig. 8 An example of database cracking

Figure 8 displays an example of database cracking. The database cracking was implemented in one ‘MonetDB’ as a successful alternative to index scanning [7, 28]. However, the database cracking was observed to be CPU-intensive rather than I/O bound. Pirk et al. [28] proposed an enhancement that has taken the database cracking from being CPU bound to I/O bound. Input/output bound operation is best suited for big data processing. The authors used approaches such as predication, vectorization, data parallelism and CPU multi-thread on SIMD instructions to achieve the method. The results of their work showed that it is 25 times faster than the first database cracking.

3.3 Adaptive Merge Graefe, Goetz, Kuno and Harumi [18] have introduced a modification of database cracking called adaptive merge. The adaptive merge also creates index by using the predicates specified in a query and used the partial index to answer that given query. The process also increments and optimizes the index with the help of the underlining data structure, the partitioned B-Tree. Adaptive merge uses merge sort for the index optimization and works based on ‘monitor queries then build index’ approach.

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Fig. 9 An adaptive merge index is based of B-Tree

Unlike database cracking that uses partitioning of its data structure and works only on in-memory database, the adaptive merge works on external block access like flash memory and disc as well as in-memory. In terms of performance, the adaptive merge indexing optimizes with far less number of queries compared to database cracking. This is due to the fact that merge process’s fan-in is unlimited as against limited fan-out of partitioning [18, 22]. Then Idreos et al. [22] made a further attempt to bring the two approaches into one, by replacing the index initial creation stage of the adaptive merge. The authors suggested a data structure that does not require much resource to build such as array, but optimizes by merging. This attempt was believed to work well with bug datasets. Figure 9 presents the partitioned B-Tree that was generated by adaptive merge index. The last three indexing methods also referred to as oblivious indexes work by creating the index on-the-fly and automatically. The indexes use the concept of monitoring any issued query and then build the index as the side effect of the query’s execution. However, all the above-discussed indexing techniques work with RDBMS and in an OLTP approach. The OLTP usually has short queries that are frequently posed to the database as the main operational system. So, there are number of arriving queries to increment and/or optimize an index if the need arises. On the contrary, the situation is different in the case of OLAP, especially in batch-oriented situations, which are the most common analysis when it comes to big data. Also, the mentioned cases are mostly different when MapReduce is to be used for such analysis. The drawbacks of the RDBMS in processing big data imply that their corresponding index types have the same drawbacks. This prompted researchers of big data to customize and, in some cases, develop different indexing strategies for the big data analysis as mentioned earlier. These developed indexing strategies for big data information retrieval systems are being used by big data analytics.

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3.4 Big Data Analytics Platforms Upon the realization of the drawbacks of the traditional database systems, research efforts continue with more focus in the direction of fast processing of big data, especially with OLAP queries (big data analytics). The analytical challenge of big data indicates that it has outgrown that capacity of traditional DBMS resources. This further prompts for the development of new technologies to solve the challenge. As a result of the above, Dean and Ghemawat [11] had presented the notable new technologies of Google File System (GFS), which is a distributed file system used for data storage across clusters. The authors also introduced the Google MapReduce programming model, which is used for data analysis and indexing [9, 10, 14, 23]. The MapReduce is a programming paradigm that uses divide and conquer approach to problem-solving. MapReduce uses functional programming approach with only two functions: Map and Reduce. The functions are deployed to the chunks of data that are stored on the distributed file system. The tasks to be carried out by the functions are to be defined by the user and they are executed in parallel on the various blocks of data. The MapReduce works in a key/values filtering and aggregation manner to solve the problem in question [19, 23, 24, 36, 43]. The MapReduce remained the most powerful and the most accepted approach for the big data mining and analytics [24, 37]. One of the major reasons for its popularity among researchers and industry experts is its flexibility. Users can intuitively write code to solve virtually any problem. Besides that, MapReduce also supports very high parallel programming, even though at low level. MapReduce design nature was to eradicate input/output (I/O) problem associated to extract, load and transfer (ELT) [23]. The design migrates the computation to various computing units instead of moving data into memory for computation. Furthermore, the Hadoop MapReduce implementation remains the fastest, one reported, in handling very large data analysis. In addition, larger percentage of research works’ experiment involving big data analytics that are found in the literatures are done using the Hadoop MapReduce or they are related to it or its associated tools. Lastly, reports and releases coming from the big IT companies indicate that most of them use tools that are supported by the Hadoop/MapReduce in performing their big data analysis [24, 31, 33, 37]. Table 1 highlights some of the features of three attempted solutions for big data analytics.

4 Inherent Indexes in MapReduce A simple inverted index is said to be inherently and trivially implemented in MapReduce as one of the effective tasks for textual data retrieval. This is highlighted by Dean and Ghemawat [11] in their original paper on MapReduce and cited by Graef and kuno [18]. When performing inverted index with MapReduce, the map function parses the split covering certain input, and emits sequence of pairs.

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Table 1 Comparison between big data analytics approaches S. no.

Big data analytic

Parallel programming approach

Dynamic flexibility

Schema less

SQL support

Extract, load and transfer

1

MapReduce

Yes

Yes

Yes

No

Not directly

2

Algebraic workflow [12]

Yes

No

Yes

Yes

No

3

AterixDB [1]

Yes

No

No SQL Like

Semi-structured

Yes

The reduce function accepts all pairs of the same key, sort the corresponding values and emits pair. The set of all output pairs form a simple inverted index. McCreadie [25] deducted that two interpretations of the above scenario can be a per-token indexing or a per-term indexing in relation to the indexing of corpus datasets. The per-token indexing strategy involves emitting of pairs for each token in a document by the map function. However, the reduce function does the aggregation of each unique term with its corresponding doc-ID to obtain the term frequencies (tf), after which the completed posting list for that term is written to disk. So, if a term appears tf times it will be indicated as such. The advantage of this strategy is that it makes the map phase very simple. However, it has the potential of costing more memory and time due to storage of large intermediate results, network traffic and prolonging sort phase (Fig. 10). On the other hand, the per-term indexing uses the map function to emit tuple in the form , and this reduces the number of emit operation as only unique term per document is emitted. The reduce function in this inter-operation only sorts the instances by document to obtain the final posting list sorted by ascending doc-ID. Ivory information retrieval system uses this approach. Also, a combiner function can be used to generate tfs by performing a localized merge on each map task’s output (Fig. 11).

4.1 Per Document Indexing This is the inverted indexing technique used by Nutch platform on the Hadoop to index document for faster search. Nutch tokenized the document during map phase and the map function emits tuples in the form of , while the reduce phase writes all index structures. Though this strategy emits less, the value of each emit used to have more data and have reduced intermediate results, thus achieving higher levels of compression than single terms. Documents are indexed on same reduce task easily due to the sorting of document names [25].

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Fig. 10 A sample of per token index using MapReduce

4.2 Per-Posting List Indexing This indexing technique is based on a single-pass indexing, which splits data onto multiple map tasks, with each operating on its own data sub-set. The map task serves as the scanning phase of the single-pass indexing. As this process run on the document, compressed posting lists are built in memory for each term. This partial index is flushed from the map task when the memory run low or when all the documents are processed. The flushing is done by emitting a set in the form of pairs for all terms present in the memory. Before taking up of intermediate results by reduce task, the flushed partial indexes are sorted and stored on disk first by map number and then by flushed numbers. In order to achieve globally correct ordering of posting list for each term, the posting lists are merged by map number and flushed number. The term, posting lists, is merged together by the reduce

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Fig. 11 A sample of per term index using MapReduce

function to form the standard index comprising full posting lists. The standard index is compressed using EliasGamma technique by storing only the distance doc-IDs [25] (Fig. 12). All of the four strategies of inverted indexing in MapReduce discussed above are the main task focused on and carried out by the MapReduce job. This is against the primary function of indexing in RDBMS, which is to speed up access of stored data in order to improve the performance of other processes. Thus, the aim of indexing is not only to use but also to improve parallel processing of the document contents. Rather, the primary aim of indexing is to improve the performance of parallel processor itself. This improvement is to be achieved in addition to the underlining parallel processor that MapReduce is programmed to accomplish. Thus, there is a need for additional indexing scheme that works with the parallel processor and serves the same purpose with what index does in RDBMS.

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Fig. 12 A sample of per document index using MapReduce

5 User-Defined Indexing in MapReduce For the user-defined indexing used in MapReduce and big data analytics, Yang and Parker [40] have employed HDFS’s file component as B-Tree nodes to achieve indexing. In their approach each file contains data and pointer to lower files in the tree hierarchy, which is considered its children. During query processing the tree is traversed to locate the required segment of data to be processed using an improved Map-Reduce-Merge-Traverse version of MapReduce. After locating the data, then the map, the reduce and the merge tasks are performed on it to return the record set that answers the given query. Also, An et al. [3] have used blockIds from the HDFS as the search keys of their B+-Tree-based index. When a given query is to be processed, the B+-Tree-based index is first searched to determine the start and the end of contiguous blocks that formed the index, and the result of the search formed the input data to be scanned. Then, only the blockIds that are returned from such search are used by MapReduce for main query processing. Hence, through this process preventing the full scan of

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the input data is done. In addition, Richter et al. [30] used the copies of replica stored by HDFS, to index different data attributes, which may likely be used as incoming query’s predicates. When a MapReduce query arrives, their library checks the fields contained in the query’s predicates and used the clustered index built on that field to return the blockIds of the data required to answer the given query. Furthermore, in all the mentioned studies, the authors used indexing data structure that scale logarithmically, thereby improving data processing and retrieval. This is done by preventing the MapReduce from full scan of input data by guiding the process to just scanning and processing the data that corresponds to the output of the indexes. Moreover, many other researches were conducted on indexing using different types of big data; however, those researches are closely tied to that type of big data as the indexing data structure and the index implementations are determined by the nature of the data itself [20]. Table 2 displays the summary of the index approaches, their memory requirement and big data potentials. Table 2 Comparison between big data analytics approaches Reference

Indexing approach

Data structure

Memory/storage

Potential for big data mining

Wu et al. [39], Fusco et al. [13]

Bitmap

Tabular

Requires large Has good space of memory potential for big and storage data mining

Gracia et al. [15], and Silberschez et al. [35]

Dense

Tabular

Requires large Not a good space of memory approach for big and storage data mining

Gracia et al. [15], Rys [32] and Silberschez et al. [35]

Sparse

Tabular

Requires large Not a good space of memory approach for big and storage data mining

Chaudhuri et al. [8]

Online indexing

Vectors

Requires small A good approach space of memory for big data and storage mining

Idreos et al. [21]

Database cracking

Arrays

Requires small A good approach space of memory for big data and storage mining

Graefe et al. [18]

Adaptive merge

Tree-based

Requires average A very good space of memory approach for big and storage data mining

Yang and Parker [40]

B-Tree

Tree-based

Requires average A good approach space of memory for big data and storage mining

An et al. [3]

B+-Tree

Tree-based

Requires small space for of memory and storage

Richter et al. [30]

HAIL

File-based

Requires large A good approach space of memory for big data and storage mining

A good approach for big data mining

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6 Conclusion Moreover, it can be simply deducted from the above reviews that user-defined index and content indexing as tool for optimizing the performance of information retrieval has been successful. It can also be added that there is high potential for improving big data analytics through the use of advanced indexing techniques and data structures. Particularly, if these techniques and data structures are hybridized, improved and customized to work with MapReduce, they will surely improve the performance of big data analytics. It has been highlighted that MapReduce is one of the most popular tools for big data analytics. However, the low-level nature of its implementations has given rise to the development of HLQLs. The HLQLs ease the programmer’s task of handling the analysis. The review also highlighted the different types of index in both RDBMS and those used in big data analytics using different big data analytics platforms, including MapReduce and its index approaches. The improvement can be achieved by changing the indexing approach or using more efficient data structure.

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Two-Steps Wrapper-Based Feature Selection in Classification: A Comparison Between Continuous and Binary Variants of Cuckoo Optimisation Algorithm Ali Muhammad Usman, Umi Kalsom Yusof, and Syibrah Naim

Abstract Feature selection (FS) is the process of eliminating irrelevant features and improving classification performance while maintaining the standard of the data. Based on evaluation criteria, FS can be either filter or wrapper. Wrappers are computationally expensive due to many feature interactions in the search space. In this study, cuckoo optimisation algorithm (COA) along with its binary (BCOA) are used as wrapper-based FS for the first time to explore the promising regions in the search space, which obtained improved classification accuracy and selected better quality subsets of features within a shorter time. Based on that we developed two different fitness functions. The first one (BCOA-FS) and (BCOA-FS) adopt the standard wrapper-based evaluation with emphasis mainly on the classification performance. Whereas in the second one (BCOA-2S) and (COA2S) combine the first one in another evaluation process with a focus on both the number of features and classification accuracy. The results obtained indicate that COA-FS and BCOA-FS can select fewer features with better accuracy on both categorical and continuous label data, with BCOA-FS better than COA-FS. Similarly, COA-2S performed better than BCOAFS and COA-2S and is comparable to the existing works. BCOA-2S outperformed the three of the existing studies on the majority of the datasets with almost 10 and 5% on both classification accuracy and number of selected features, respectively. Keywords Feature selection · Wrapper-based · Cuckoo optimisation algorithm · Binary Cuckoo optimisation algorithm and Classification A. M. Usman · U. K. Yusof (B) School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Penang, Malaysia e-mail: [email protected] A. M. Usman e-mail: [email protected] A. M. Usman Department of Computer Sciences, Federal College of Education (Technical), P.M.B 60, Gombe, Nigeria S. Naim Technology Department, Endicott College of International Studies (ECIS), Woosong University, Daejeon, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_6

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1 Introduction In classification, feature selection (FS) is mainly used to minimise the features in a data set while maintaining its standard [23, 68]. The aim is to select the most relevant subsets that are sufficient enough to describe the target class [62]. FS can be supervised [51], non-supervised [12] and semi-supervised [61]. In dealing with the supervised type, the class label of the data set is defined already in contrast with the non-supervised that the class label is unaware. Whereas, semi-supervised combine both supervised and non-supervised (i.e. with both label and unlabelled data) [32]. The supervised FS is classified further as a filter, wrapper and hybrid approach depending on their evaluation criteria [64]. In the filter-based approach, the features are evaluated without considering any classification algorithm, which makes them computationally fast. However, the filter ignores feature dependence or relationship among selected or ranked features, which subsequently affects the classification performance (i.e. either error rate or accuracy) [15]. In the wrapper-based method, a classification algorithm is used to evaluate each subset of features selected, and hence, it achieves better classification accuracy or error rate [26, 32, 64]. The major shortcomings of the wrapper-based approach are computationally expensive and not favourable on high-dimensional data sets. The most common examples of the wrapper-based approach are sequential forward selection (SFS) [60], sequential backward selection (SBS) [36] plus q take away r [16] and genetic algorithm (GA) [37] among others. In FS, the classification performance of any of the approach (filter or wrapper) is measured in according to feature size (number of selected features), error rate (or accuracy) and computational time. Machine learning algorithms are commonly used to measure or evaluate the goodness of the selected subset of features in terms of accuracy or error rates [64]. Examples of the most widely used ones include support vector machine (SVM) [57], K-nearest neighbour (KNN) [24] and Gaussian Naïve Bayes (GNB) [34] among others. The wrapper-based approach requires one to determine a classification algorithm and uses its performance as the evaluation standard. It searches for features that are suitable to the machine learning algorithm that increases the accuracy [39, 40]. Classification accuracy, as well as the selected subsets of features, are used to determine the prediction performance in wrapper model [54, 66]. As such, prediction accuracy and less prone to local optima are the critical advantage of wrapper over the filter. Hence, the outcomes are mostly more encouraging than the findings of the filter models. However, the shortcomings of the wrapper-based approach include a high risk of overfitting data, classifier dependency, highly computationally intensive and not favourable for astronomical dimensional data [41]. Examples of wrapper techniques are a sequential forward selection (SFS), sequential backward elimination (SBE), plus q take away r and beam search. Others include simulated annealing, randomised hill-climbing, genetic algorithm and estimation of distribution algorithm among others [40, 46]—the steps on how the wrapper-based approach is illustrated in Fig. 1.

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Fig. 1 Wrapper-Based feature selection procedure

Finding the optimal subsets of features with the less computational cost is quite a demanding task because of the search space, and the number of features needed to search in the solutions are too many. Hence, FS is considered as an NP-hard problem [33, 39, 52, 64]. In searching for the best subsets of features, [26, 39] identified three search strategies: complete search, sequential or heuristic search and the random search. The complete search works by finding all the possible feature subsets while evaluating them one after the other to select the best subset of features with the highest classification performance. There is a guarantee of getting the optimal results based on the laid down criteria in it. However, for a data set with N number of features, there will be 2 N subsets to be generated and evaluated, which is almost impractical for a considerable value of N . More so, [41] argued that ‘search is complete does not mean that it must be exhaustive’. The heuristic or sequential search add or remove features in sequential order; the remaining features that are not selected are considered later for selection in that manner. By doing so, the choice of the features may likely end up with the same pattern as complete search. However, there is no guarantee of finding the target solution [52].

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Random search is the most popularly used among all the search strategies [42]. It aims at creating stability between the heuristic and complete search by combining the advantages of both. It begins with a randomly selected subset of features and progress in two ways. Either to follow the heuristic search and insert some randomness or to generate the next subset in a completely random manner [39, 42]. Out of all these search strategies, the random search is the only one that can escape from local optimum in the vast search space due to the involvement of randomness and mostly finish within the shortest time [26, 40, 64]. There are some works on the wrapper-based FS that applies different search strategies on different meta-heuristic algorithms. For example, [9, 10] used an artificial immune system for the FS. Also a particle swarm optimisation (PSO) is reported in [31, 44, 52–54, 63, 65]. Recently, differential evolution (DE) in [27], cuckoo search in [13, 18], grasshopper optimisation algorithm in [43], genetic algorithm (GA) in [3, 19, 30, 59, 66] are reported. In addition to that, genetic programming is used for the wrapper-based FS in [48], and recently a flower pollination algorithm for FS as also in [56]. It clearly shows that the meta-heuristic algorithms are suitable for addressing these problems. Despite the attempt to solve the lingering issues of the wrapper-based FS still, the existing works cannot successfully evolve the best subset of features with improving accuracy on some of the data sets [23]. The cuckoo optimisation algorithm (COA) presented in [49] is among the evolutionary algorithms that show promising results in handling different combinatorial optimisation problem including NP-hard, despite its proven records, especially in dealing with filter-based FS in [55]. Its application, specifically for the wrapperbased FS, is not fully investigated. This study aimed to find the best subsets of features with lesser feature size and yet maintain the same or even better classification accuracy compared to using fulllength features within a short period. Also, investigate the difference between COA and BCOA in wrapper-based FS. To accomplish this goal, a pair of two FS frameworks are developed based on BCOA and COA. These proposed algorithms were studied and compared with other FS algorithms presented in other works on benchmark problems of varying difficulties. Precisely, this study will examine 1. Whether adopted COA wrapper-based FS algorithms would choose the best subsets of feature, that has least feature size, less computational and accomplish the best error rate compared to full-length features, and would outpace the adopted BCOA wrapper-based single objective algorithms; 2. Whether adopted BCOA wrapper-based FS algorithms would choose the best subsets of feature and can attain the best performance than the adopted COA wrapper-based algorithms above; 3. Whether COA wrapper-based algorithm with two steps evaluation would choose sets of best features subsets and would outpace the two steps BCOA wrapperbased algorithm, and other existing works; and

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4. Whether BCAO wrapper-based algorithm with two steps evaluation would choose sets of best features subsets and would outpace all other approaches stated directly above. The rest of the paper is prearranged as follows: Part 2 is the background containing the details about the adopted COA and BCOA along with related works. The proposed wrapper-based feature selection approaches are presented in Part 3, while Part 4 is the experimental design, data sets used along with benchmark approaches. Then Part 5 is the presentation of the results while Part 6 concludes the entire work and suggests future work areas.

2 Background 2.1 Cuckoo Optimisation Algorithm The original Cuckoo Optimisation Algorithm (COA) is strictly made for a continuous optimisation problem. At the same time, the binary version (BCOA) can be applied to solve problems that are in binary or discrete form. COA used for FS is very scarce in the literature. The size or dimension of the search space (i.e. the full-length features in every data set) is n. Every habitat in the COA is assigned by using a vector of n decimal numbers. The location of habitat i in dth length is xid normally in the range [0, 1]. To know in case if a feature is selected or otherwise, a verge 0 < θ < 1 is mandatory to equate it with the decimal numbers in the habitat position. If eventually, xid > θ , then feature d is chosen else d is not be chosen. COA developed by [49] is adopted, and the detail of how it works is 1. An array called “habitat” is used for the optimisation problem as show in Eq. 1. habitat = [x1 , x2 , . . . , x N var ]

(1)

2. Five and twenty eggs are used as the lower and upper limits, respectively, for every iteration. 3. They lay their eggs within a maximum range distance from their habitat in Equation. number o f curr ent cuckoos × enew (2) ELR = α × total number o f eggs An α represent an integer number. 4. P% (those without any profit value) of the laid eggs are killed. 5. A k-means (K = 3 or 5) clustering is used for the grouping. 6. All cuckoos deviate ϕ radians while flying λ% to the goal, as shown in Eq. 3. λ ∼ U (0, 1)

ϕ ∼ (−ω, ω)

(3)

where λ U (0, 1) means that λ is a uniformly distributed random within range of 0 and 1. ω is limits an aberration from goal habitat.

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2.2 Binary Cuckoo Optimisation Algorithm Binary Cuckoo Optimisation Algorithm (BCOA) is mostly used to solve FS problem; meanwhile, the representation of the habitat is in the form of a binary string, where the position of every habitat is a boolean 1 which signifies that a feature is chosen and 0 otherwise. Assuming X G and X C represent the respective goal and current habitat. Then, Eq. 4 computes the X N H next habitat as follows: X N H = X C + rand(X G − X C )

(4)

A sigmoid function is applied in Eq. 5 to use X N H as binary to record it within [0, 1]. Then Eq. 6 alters the values to either 0 or 1. S=

1 (1 + e−X N H )

(5)

I F (S > rand) T H E N X N H = 1 AN D I F (S < rand) T H E N X N H = 0 (6)

2.3 Related Works This part reviews some related works on wrapper-based FS. Both the traditional and meta-heuristic ones, as shown in the subsequent parts. However, this study focuses mostly on the evolutionary algorithms; for more details on the swarm intelligence based approaches refer to [7].

2.3.1

Classical Wrapper-Based Feature Selection

As mentioned earlier, wrapper-based FS algorithms are highly computationally cost compared to the filter-based FS algorithms [15, 48]. Perhaps, this is due to the longer evaluation processes involved in the training and testing of the classifier. Furthermore, since the search space of the FS problem is exponential to the number of features. Therefore, searching for the entire search space is impractical. Based on that the existing wrapper-based techniques used stochastic or greedy search [21, 42]. The most common FS techniques that practice the greedy hill-climbing are sequential feature selection (SFS) [1] and sequential backward selection (SBS) [41]. In SFS, it begins with an empty set of features and keeps on adding one feature at a time in an iterative manner until adding another feature will not enhance the existing classification performance then it stops. Unlike, in SBS where it starts with a full set of features and keeps on looping to remove one feature at a time until removal of a feature cannot improve the existing classification accuracy (error rate). Apart from the computational cost incurred on a large number of data sets, another major

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drawback of both SFS and SBS is the nesting effect, since any feature that is added or removed cannot be undone. Thus, they both are trapped into the local optima easily [40, 41]. Although, [38] developed a “plus q take away r” technique that will escape the nesting effect, SBS was applied r times in a back-tracking order while SFS is applied q times in forwarding step order. Determining better numbers for q and r is required, to solve this problem of having fixed values for both q and r. Then, [47] enhanced it by introducing a floating-point in both SFS (sequential forward floating selection (SFFS)) and SBS (sequential backwards floating selection (SBFS)) that automatically determine the value of q and r. Although, both SFFS and SBFS proved to be useful in some cases, [67] argued that they could likely trap into local optimal even if the benchmark function is monotonic (neither decrease nor increase) and yet is a small-scale problem. Inline spectral frequencies (LFS), the number of features to be used for evaluation in every step are limited. As such, the computational efficiency of the sequential forward’s methods was enhanced by the LFS and sustained an analogous accuracy of the selected subset of features. But, LSF ranks all features without taking into consideration whether some features are present or not, and this restricts the performance of the LSF algorithm particularly the interaction between features.

2.3.2

Wrapper-Based Feature Selection with Meta-Heuristic Algorithms

As mentioned earlier, meta-heuristic algorithms have become more robust in handling NP-hard problems, including FS. Huang and Wang [30] employed GA for both FS and SVM parameter optimisation on a real-world data set. The results obtained are in favour of the GA in terms of classification accuracy and fewer number of features compared with the grid algorithm reported in work. Also, [59] proposed another GA for FS and SVM for parameter selection in the detection of diabetic retinopathy. A promising result was obtained on 60 images of data sets. An enhanced GA (EGA) was proposed in [19] to reduce text dimensionality. It is incorporated with six filter FS methods to create a hybrid one. Finally, experimental results showed that the hybrid outperformed the single approach as well as the traditional GA. Recently in [3], the highest accuracy of 99.48% was attained on two different Wisconsin breast cancer data sets. GA was used for FS before applying the five different classifiers. The results obtained are better than the others. Unler and Murat [53] present a discrete PSO for FS in binary classification problems. The proposed approach incorporates an adaptive FS technique which dynamically takes into consideration the relevance and dependence of the features included in the feature subset. The experimental results indicated that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance compared with the scatter search and tabu search algorithms on openly available data sets.

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Vieira et al. [58] proposed a modified binary PSO (MBPSO) for FS with simultaneous optimisation of SVM parameters to predict the outcome of patients with septic shock. The results indicated that MBPSO performed very well compared with the standard PSO both in terms of accuracy and features selected. However, when compared to GA, the same accuracy was recorded, but the MBPSO select fewer features. Similarly, [31] developed a supervised PSO-based rough set FS for medical data diagnosis. Two different algorithms PSO-based relative reduct (PSO-RR) and PSObased quick reduct (PSO-QR) are presented. The results obtained showed that the proposed algorithms performed better in terms of the fewer number of features, classification accuracy and computational time compared to the standard PSO and other methods reported. Recently, PSO initialisation and updating mechanism are changed to suit better FS problems in [52]. The discretisation is applied before the FS since discretisation is considered an essential task of FS. A potential particle swarm optimisation (PPSO) is proposed which employs a modern illustration that can minimise the search space of the problem and an advanced fitness function to assess candidate solutions better and direct the search process. The results of the experiments on the ten high-dimensional data sets disclosed that PPSO chooses fewer than 5% of the number of features for all data sets. Compared with the two-stage method which uses bare-bone PSO (BBPSO) for FS on the discretised data, PPSO attains a better accuracy on seven data sets. Furthermore, PPSO gains improved classification accuracy than evolve PSO (EPSO) on eight data sets with a reduced feature size on six data sets. Moreover, PPSO also performs better than the three compared approaches and achieves similar to one approach on majority data sets in terms of both learning capacity as well as generalisation ability. To predict heart disease among patients, [18] used cuckoo search and rough set for FS, and the disease prediction is made using fuzzy. A better result was achieved in four different benchmark data sets. Recently, [13] used a modified cuckoo search along with rough set to build the fitness function that takes several features into the reduce set and classification into consideration. SVM and KNN are used to evaluate the performance of the proposed approach. The results obtained indicate the superiority of the method used and can significantly improve performance. Despite the attempt to solve the lingering issues of the wrapper-based FS, still the existing works cannot successfully evolve the best subset of features with improving accuracy on some of the data sets [23]. COA presented in [49] is among the evolutionary algorithms that show promising results in handling different combinatorial optimisation problem, including NP-hard; However, despite its proven records, especially in dealing with filter-based FS in [55]. COA has been applied to solve different kinds of problems. Recently, it is used with harmony search for optimum tuning of fuzzy PID controller for LFC of interconnected power systems in [20]. Energy-aware clustering in wireless sensor networks in [35], accelerated COA was proposed in [22] where simulated annealing algorithm

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was used in place of the k-means clustering of the standard COA in vehicle routing problems. Compared to GA, the imperialist competitive algorithm (ICA), CSA and PSO. COA is simpler to implement and can converge rapidly [6, 29]. Its application, specifically for the wrapper-based FS, is not fully investigated.

3 Proposed Wrapper-Based Feature Selection Approaches Thus, in this part first, both BCOA and COA are adopted and used for wrapper-based FS. The detail of how each of the experiments was carried out can be seen in the subsequent parts.

3.1 BCOA and COA for Feature Selection Two wrapper-based FS are proposed, namely, BCOA-FS and COA-FS. Throughout the evolutionary training process, Eq. 7 is applied as the fitness evaluation function to estimate and evaluate the best cuckoo habitat i, where the position xi signifies the subsets of features. (7) Fitness(x(i) ) = Err or Rate where ErrorRate is calculated based on Eq. 8: Err or Rate =

(F P + F N ) (T P + T N + F P + F N )

(8)

where F P, F N ,T P, and T N , are the respective false positives, false negatives, true positives, and true negatives.

3.2 A Combined Fitness Function for BCOA and COA Feature Selection The subset of feature selected by both BCOA-FS and COA-FS may probably comprise some redundancy since the fitness function in Eq. 7 does not reduce the features. However, it hypothesises that the same or less accuracy might be realised using a smaller subset of features. To additionally minimise the feature size deprived of affecting the classification error rate, a two-step FS method (BCOA-2S and COA2S) is introduced, where the entire evolutionary procedure is separated into two steps.

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In step 1, both BCOA-FS and COA-FS emphasises on improving accuracy. Whereas, in step 2, the features are involved in the fitness function. Furthermore, step 2 begins with the solutions realised in step 1, which certifies the reductions in the features according to the subsets of the features with the best accuracy. The proposed two-step fitness function employed in both BCOA-2S and COA-2S is shown in Eq. 9:

Fitness2 (xi ) =

⎧ ⎨ Step 1, Err or Rate ⎩ Step 2, Err or Rate β ∗

M + (1 − β) ∗ n

M Er or Rate n Er or Rate

(9)

where Error Rate is the classification error rate attained by the selected subset of features. β ∈ [0, 1] is a constant number within the range [63]. M denotes the size of selected features and n is the total feature size. n Err or Rate is the error rate obtained by using the total feature size for classification on the training set. In step 2, the fitness function considers both the feature size as well as the error rate. It guarantees that these two components are in a similar array, i.e. [0, 1], and the of feature size is normalised and represented by M/n. The classification performance is represented by (M Err or Rate)/(n Err or Rate) rather than Error Rate alone to circumvent the circumstances, whereby Error Rate is too insignificant (for instance, < 0.005), and M/n plays a significant role inside the fitness function. In a situation like this, the feature size considers most compared to the error rate, which might have a subset of features with high error rate compared to using the full-length feature size. Meanwhile Error Rate would be lesser than n Err or Rate at the end of the step 1, (M Err or Rate)/(n Err or Rate) is in the similar array as M/n, i.e. [0, 1]. As soon as they are joined into a single fitness function, β is employed to display the comparative significance of the chosen features and (1 − β) displays the outstanding significance of the error rate. The Err orrate is expected to be more significance compared with feature size, thus β is assign to be lesser than (1 − β) (i.e. β < 0.5). The pseudocode of (BCOA-FS and BCOA-2S) along with (COAFS and COA-2S) can be seen in Algorithm 1 and Algorithm 2, respectively. The main difference between BCOA-FS, COA-FS and BCOA-2S depend on the fitness evaluation function, that is illustrated mostly in the grey lines of algorithms. The detailed of the proposed wrapper-based BCOA is depicted in Algorithm 1, whereby Eqs. 7 and 8 have been used as the respective fitness functions. The grey colour signifies the areas where the equations and initialisation as per feature selection problems are used in the proposed algorithms.

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Figure 2 shows that each cuckoo is initialised according to Eq. 1 for each of the data sets. Then, the number of features in each habitat are collected while those eggs detected in the habitat are killed. At this juncture, the fitness function for both BCOA-FS and COA-FS are evaluated using Eqs. 7 and 8, respectively.Whereas, the fitness function for the combined FS (BCOA-2S and COA-2S) are evaluated using

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Fig. 2 Flowchart of the (BCOA-FS and BCOA-2S) and (COA-FS and COA-2S)

the fitness function in Eq. 9. The population is compared with maximum value, and if the population is less than the maximum value, then the cuckoo in the worst area would be killed, otherwise it gets profit values (check the survival of the egg inside the nest). Then stop condition evaluated; if yes, it leads the eggs to grow. However, the nest found with the best survival rate among the cuckoo societies is transferred to the best society according to Eqs. 5 and 6 for the BCOA. Whereas, Eqs. 2 and 3 are for the standard COA. Based on Eq. 2, one can find the best ELR and repeat all the steps. Otherwise, return the optimum solution of the highest ranked features. Then, finally, the best, along with the average of them, are selected using a classifier.

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The time complexity of both BCOA-FS and COA-FS based on the fitness function in Eq. 7 is O( m1 ) + O( n1 ). The term m and n represent the number of selected features and the population size, respectively. The binary search for using BCOA runs in O(n) time, while the COA search in O(log2 n) time. Thus, the computational complexity of BCOA-FS is O( m1 ) + O( n1 ) + O(m) and COA-FS is O( m1 ) + O( n1 ) + O(log2 n). Based on the fitness function in Eq. 8, the complexity is O( m12 ) + O( n12 ). Therefore, the total complexity of the BCOA-2S is O( m12 ) + O( n12 ) + O(n) and that of COA-2S is O( m12 ) + O( n12 ) + O(log2 n). Therefore, BCOA-FS and COA-FS can complete its process within a shorter time in most cases compared to its BCOA-2S and COA-2S counterpart.

4 Experimental Design This part describes the data sets used in conducting the experiments. Parameters settings, as well as benchmark, approaches are used to test the performance of the proposed methods.

4.1 Experimental Datasets The data sets used in this study are the 26 well-known University of California Irvine (UCI) Machine learning data sets with distinct features. It contains a different number of features ranging from 9 to 500, 14 categorical and 12 continuous data type, 72–5000 instances, 13 binary classes and 13 multi-classes. These different appearances of the data set, especially on the number of features that contain smaller, medium and large features are the motives behind the selection of the data sets as shown in Table 1. The data sets can be found in [17] or can be downloaded freely at https://www.ics.uci.edu.ml/~earn. Furthermore, most of the data sets have been used recently in the works of [2, 15, 43, 44], which clearly show that the data sets are goods for benchmarking FS problems. The data sets contain both categorical and continuous data that can be useful in demonstrating the comparison between the categorical discrete and the continuous data. Continuous data have infinite values in the form of decimal numbers, while the categorical discrete values are mostly finite values in groups.

4.2 Experimental Parameter Settings The parameters employed for the experiments were set as follows: initial and maximum population are set to 5 and 20, respectively. Moreover, the proposed algorithms were run 40 independent times on each data set. The parameter settings used for the

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Table 1 List of data sets S/N Data set 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Wine Australian Zoo Vehicle Lymphography (Lymph) Mushroom Spect German Leddisplay WBCD/BreastEW Ionosphere (Ionosp) Dermatology Soybean Large Chess (KrvskpEW) Connect4 LungCancer (Lung) Promoter Sonar Splice Optic Audiology Coil2000 Hillvalley Musk1 (Clean1) DNA Madelon

Features

Classes

Instances

Data type

13 14 17 18 18

3 2 7 4 4

178 690 101 846 148

Continuous Continuous Continuous Continuous Categorical

24 22 24 24 30 34 34 35 36 42 56 57 60 60 64 68 85 100 166 180 500

2 2 2 10 2 2 6 19 2 3 3 2 2 3 10 24 2 2 2 3 2

5644 267 1000 1000 569 351 366 307 3196 44473 32 106 208 3190 5620 226 9000 606 476 3186 4400

Categorical Categorical Continuous Categorical Continuous Continuous Categorical Categorical Categorical Categorical Continuous Categorical Continuous Categorical Categorical Categorical Categorical Continuous Continuous Categorical Continuous

proposed C O A − F S, C O A − 2S, BC O A − F S and BC O A − 2S algorithms are chosen based on the work of [45, 49]. The maximum number of iterations was set to 100. Also, similar to the work of [63] and [65]. In the experiments, all the rows in each of the data sets were partition into two groups: a training group and a test group. The most partitioning approach is that 2/3 (about 66%) of the rows in the data sets are in the training group and 1/3 (almost 33%) of the rows are in the test group [11]. To simplify the process, we divide 70% of the rows into each data set as the training group and the remaining 30% as the test group. The rows are chosen so that the percentage of rows from various classes are equal in both the training group. The proposed wrapper-based methods need a classifier to estimate the suitability of the

Two-Steps Wrapper-Based Feature Selection in Classification … Table 2 Existing wrapper-based approaches References Type [8] [9] [63] [27]

Single-objective Single-objective Single-objective Single-objective

159

Acronym

Year

GSBS BAIS ErFS and 2SFS ABC-ER, ABC-Fit2C

1994 2009 2012 2018

selected subsets of features. A KNN (with K = 5) was used in the experiments, to reduce the wrapper-based computational time [4]. The experiments of GSBS and LFS are carried out using the popularly known Waikato Environment for Knowledge Analysis (WEKA) [28]. The entire settings in LFS along with GSBS are saved to the defaults since they can obtain better results. Also, a 5NN was used in both LFS and GSBS, which generate a unique solution (feature subset) for each data set.

4.3 Benchmark Approaches Scrutinise the concert of the proposed wrapper-based approaches in this chapter. The results found are related to the previous works, as shown in Table 2. From (Table 2), two traditionally known wrapper-based FS methods, namely linear forward selection (LFS) [25] and greedy stepwise backward selection (GSBS) [8] are used as benchmark methods. Both LFS, together with GSBS, were consequential of SFS and SBS, respectively. LFS [25] limits the number of features that are selected in each step of the forward selection, which can reduce the number of evaluations. As such, the LFS is computationally less expensive compared to the SFS and will get better results. More details about the LFS is in [25]. On the other hand, the greedy stepwise based FS algorithm mostly shifts either forward or backwards in the search space [8]. Provided that the LFS makes a forward selection, a backward search is selected in the greedy stepwise search to create a greedy stepwise backward selection (GSBS). GSBS begins with all the feature size and halts if the removal of any outstanding feature results in a reduction in evaluation measure, i.e. the error rate of the classifier. Also, the work in [63] was used as a benchmark method for both single and multi-objective wrapper-based approach, due to the similarities in the data sets. The detail explanation of the results obtained and the analysis is presented in the subsequent sections. The details of the results obtained are offered in the subsequent section.

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5 Results and Discussions This part deliberates on the results of the proposed methods, comparison between them and other existing works that their work coincide with the data sets apply in this study.

5.1 Results of the Proposed BCOA-FS and COA-FS The results of both the categorical and continuous data sets for BCOA-FS and COAFS are displayed in Tables 4 and 3, respectively. The results showed a comparison between all the proposed wrapper-based methods. From the tables, “BCOA-FS” and “COA-FS” represent the proposed wrapper-based methods that adopt both BCOA and COA, respectively. “All” stands for all features used for each of the data sets. Besides, “Ave Size”, “Ave Acc” and “Best Acc” represents average feature size, average accuracy and best accuracy attained by each of the data sets for the 40 independent runs, respectively. The results proposed BCOA-FS outperformed its COA-FS counterpart on the continuous data sets. Out of the 12 data sets in the table (Table 3), they recorded similar feature size, best accuracy and average accuracy on WineEW, Australian, Zoo and to some extents on Vehicle data sets. However, as the number of features increases, BCOA-FS outperformed COA-FS on the remaining eight data sets. In addition to that a similar performance was slightly noticed between BCOOA-FS and COA-FS on HillValley datasets. On the average, it is clear that BCOA-FS outperformed its COA-FS counterpart on the majority of the data sets, and hence considered the best wrapper-based feature selection. Alternatively, a comparison between BCOA-FS and COA-FS was made on categorical data sets, as shown in the results Table 4. Similar to the continuous data sets, the categorical data sets also recorded similarities in terms of the mean of selected features, best accuracy and average accuracy on data sets with fewer feature size as such as Lymph, Mushroom, Spect and Leddisplay. However, from Dermatology that has 34 total number of available features, there is a change in performance between BCOA-FS and COA-FS. The results also imply that as the feature size increase the BCOA-FS perform better than the COA-FS in all the data sets except in Coil2000. Perhaps due to a large number of instances in the Coil2000 data set.

5.2 Results of the Proposed BCOA-2S and COA-2S The results of both the categorical and continuous data sets for BCOA-2S and COA2S are also displayed in Tables 4 and 3, respectively. The terms “BCOA-2S” and “COA-2S” represents the proposed combined accuracy and selected features into a

IonosphereEW

Germany

30

12.56

12.12

11.85

10.65

BCOA-FS

COA-FS

COA-2S

BCOA-2S

11.2

BCOA-2S

5

11.5

COA-2S

GSBS

12.1

COA-FS

LFS

12.5

BCOA-FS

34

17

All

3

8

BCOA-2S

GSBS

8

COA-2S

LFS

9

COA-FS

24

9

BCOA-FS

All

7

BCOA-2S

8

7

COA-2S

GSBS

7

COA-FS

LFS

8

BCOA-FS

16

8

GSBS

All

8

LFS

Zoo

7

All

WineEW

Ave-Size

13

Approach

Datasets

98.25

94.26

92.46

88.82

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

100

86.21

86.21

86.21

86.21

74.57

77.25

Best-Acc

86.21

86.21

86.21

86.21

75.70

75.45

74.25

72.48

98.56

98.56

96.25

96.25

86.21

97.25

96.75

96.75

Ave- Acc

8710.02

9038.7

8227.17

7835.4

17380.8

17479.8

17180.8

17044.2

4865.4

4964.4

4665.4

4559.4

6417

6516

6217

6120

Time(s)

LungCancer

WBCD

Vehicle

Australian

Datasets

Table 3 Results of the BCOA-FS, COA-FS, BCOA-2S and COA-2S for continuos data sets Approach

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

Ave-Size

25

25.2

27.6

27

33

6

56

12.45

13.21

13.52

13.41

24

10

30

9

9

9.1

9.1

15

9

18

3.32

3.32

3.42

3.42

12

4

14

Best-Acc

95.50

92.25

90.31

90.65

90.00

90.00

70.00

100.00

98.22

94.75

95.21

85.62

84.11

95.20

91.56

91.25

89.56

89.56

78.51

84.05

84.25

90.58

90.56

89.25

89.25

70.45

71.25

71.25

95.50

92.25

89.56

88.65

100.00

98.22

92.45

91.28

91.56

91.25

85.22

85.22

87.58

87.58

86.25

86.25

Ave- Acc

(continued)

112568.2

116816

80844.75

76995

9448.416

9804.96

8813.07

8393.4

10461.6

10560.6

10261.6

10143.4

11071.8

11170.8

10871.8

10767.6

Time(s)

Two-Steps Wrapper-Based Feature Selection in Classification … 161

122

84.25

85.6

41.25

50.1

BCOA-FS

COA-FS

COA-2S

BCOA-2S

BCOA-2S

10

19.8

COA-2S

GSBS

21.9

COA-FS

LFS

24.5

BCOA-FS

166

23.42

GSBS

All

45

LFS

Musk1(Clean1)

5

All

SonarEW

Ave-Size

60

Approach

Datasets

Table 3 (continued)

Best-Acc

90.50

88.97

88.81

88.88

86.21

86.21

86.21

98.20

96.58

86.45

90.65

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

86.21

Ave- Acc

116497

120893

73611.7

70106.4

104030

107956

70623.6

67260.6

Time(s)

Datasets

Madelon

Hillvalley

Approach

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

Ave-Size

248.52

250.6

259.2

255.5

489

7

500

42.4

44.6

46.12

45.25

90

8

100

Best-Acc

83.45

81.45

79.89

80.00

51.28

64.62

70.90

74.25

70.65

64.26

68.52

49.45

57.69

56.75

81.65

79.45

77.47

80.00

70.35

68.56

59.62

65.22

Ave- Acc

210958

218918.7

150468.6

143303.4

114193.8

118503

76548.78

72903.6

Time(s)

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single fitness function for both BCOA and COA, respectively. All other headings in the table are the same as explained in the previous subsection. There are 14 categorical data sets and 12 continuous data sets that make a total of the 26 data sets used in this research. Out of all the 14 categorical data sets, BCOA-2S accomplished better results than COA-2S in terms of the average number of selected features, best accuracy and average accuracy on almost all the data sets. Although in Leddisplay data set, it has similar performance and same best accuracy on Mushroom data set. On the other hand, the results of the continuous data sets also are in favour of BCOA-2S compared to the COA-2S in the majority of the data sets. A similar performance was obtained on some few data sets such as WineEW, Australian, Zoo and Vehicle. However, as the feature size increases, the BCO-2S also performed better than COA-2S. It is in contrast with categorical data sets no matter the feature size, BCOA-2S performed better than its COA-2S counterpart in almost all the data sets regardless of the number of features in the data sets.

5.3 Comparison Between Proposed Methods and Classical Methods A result of LSB and GSBS was reported to further compare with the proposed methods. The results clearly show that LFS could select fewer number of features than GSBS in the majority of the data sets. However, GSBS achieve the best classification results in most of the data sets. Although on some data sets with a fewer number of features, they recorded similar performance. But as the number of features increases, LFS select the smallest feature and GSBS obtained the best accuracy. Comparing LSF and GSBS with the proposed wrapper-based FS, one can observe that our proposed approaches outperformed both LSF and GSBS in terms of the number of selected features, best accuracy, and average accuracy in all most all the data sets, both continuous (Table 3) and categorical (Table 4).

5.4 Comparison Between Proposed Methods and Other Existing Methods To further evaluate the performance of the proposed methods and consequently be fair in assessing the proposed wrapper-based multi-objective. Some related works with similar datasets were used for comparison, as shown in Table 2. The details of the comparison are enumerated below: 1. Comparison with ErFS and 2SFS The results of the proposed wrapper-based feature selection are compared with the one in work [63], where ErFS and 2SFS represent the BCOA-FS and BCOA-

12.5

13

8

9

BCOA-FS

COA-FS

COA-2S

BCOA-2S

9.5

BCOA-2S

24.2

10.2

COA-2S

11

13.6

COA-FS

GSBS

12.5

BCOA-FS

LFS

26

36

12

8.2

BCOA-2S

GSBS

9

COA-2S

LFS

11

COA-FS

34

10.8

BCOA-FS

All

18

Chess (KrvskpEW) All

Dermatology

10

7

BCOA-2S

GSBS

8

COA-2S

LFS

10.5

COA-FS

22

9.2

BCOA-FS

All

14

GSBS

Spect

9

LFS

Lymph

Ave-Size

18

Approach

All

Datasets

100.00

95.50

86.44

87.75

85.65

86.21

92.00

97.56

93.12

90.12

90.25

89.65

83.25

94.85

94.75

91.75

87.75

88.98

82.50

80.00

85.10

96.55

95.00

92.65

93.56

84.21

93.48

87.25

Best-Acc

98.56

93.25

85.98

86.25

95.63

92.52

89.62

89.65

91.25

88.95

84.68

86.65

94.65

95.00

90.45

92.48

Ave- Acc

53933.4

54032.4

48733.4

43833.6

17757

17856

17557

15102

10343.27

10733.58

9896.04

9424.8

9936.864

10311.84

9525.6

9072

Time(s)

Connect4

Soybean Large

Leddisplay

Mushroom

Datasets

Table 4 Results of the BCOA-FS, COA-FS, BCOA-2S and COA-2S for categorical data sets Approach

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

Ave-Size

10

12

18

15

41

6

36

13.2

7.1

12.9

12.3

24

11

35

9

9

12

12

5

7

24

7

8

13.5

12.5

18

8

24

Best-Acc

100.00

96.50

92.25

94.25

71.68

70.73

79.78

95.65

92.75

86.75

88.65

84.21

85.56

87.25

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

98.98

98.98

91.25

89.51

99.20

100.00

93.50

89.65

93.15

91.58

90.35

85.25

84.25

100.00

100.00

100.00

100.00

99.80

98.60

94.75

95.65

Ave- Acc

(continued)

53933.4

54032.4

46733.4

43833.6

20241

20340

18129.3

17188.2

18423.65

19118.88

15044.4

14328

23144.04

24017.4

16376.85

15597

Time(s)

164 A. M. Usman et al.

Coil2000

10

31

36

37.5

20.6

19.6

BCOA-FS

COA-FS

COA-2S

BCOA-2S

20.45

BCOA-2S

GSBS

26.5

COA-2S

LFS

32

COA-FS

85

30

BCOA-FS

All

38

BCOA-2S

36

12.5

COA-2S

GSBS

13.6

COA-FS

LFS

25

BCOA-FS

64

20.5

GSBS

All

50

LFS

Optic

6

All

Promoter

Ave-Size

57

Approach

Datasets

Table 4 (continued)

Best-Acc

97.75

93.65

93.21

92.65

93.98

93.58

94.55

100

100

100.00

100.00

98.75

98.69

98.88

94.50

91.25

91.89

92.11

90.00

90.00

90.00

97.75

92.52

92.01

100.00

100.00

98.98

98.98

91.25

90.65

89.68

92.11

Ave- Acc

108921.6

109020.6

78721.6

74725.2

106137

106236

75937

74176.2

107996.4

108095.4

77796.4

77416.2

Time(s)

Datasets

DNA

Audiology

Splice

Approach

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

BCOA-2S

COA-2S

COA-FS

BCOA-FS

GSBS

LFS

All

Ave-Size

52.55

48.56

90.5

88.5

173

34

180

20.9

18.54

22.4

23

24

9

68

12.4

15.6

30.5

28

47

28

60

Best-Acc

92.01

91.25

89.25

90.55

82.63

85.08

83.01

82.56

79.68

75.48

76.62

64.63

64.63

65.56

89.88

84.65

80.05

80.65

68.65

78.83

79.65

90.93

90.65

78.65

89.25

80.54

73.71

69.98

70.46

84.56

83.20

78.45

79.95

Ave- Acc

144225

144324

94025

99032.4

105220.8

105319.8

75020.8

73080

123260.4

123359.4

93060.4

92320.2

Time(s)

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2S used in this research. The significant difference between the two is the use of the EC algorithm. An outstanding EA, COA, in particular, was used in this research. Whereas, the exiting works used the most common SI based algorithm (PSO). The results indicated our proposed COA and BCOA which outperformed the existing practices of PSO used in [63]. The result is not surprising because COA is reported to be more robust and can attain better results as claimed in the work of [5, 49]. From Table 5, it is clear that all the comparisons were made on the continuous data sets. Out of the 10 data sets used, it shows that in almost all cases, our proposed methods performed better than the existing one. However, in Zoo and Ionosphere data sets, for example, the existing methods performed better in terms of average accuracy. Nevertheless, the best accuracy and the number of selected features clearly show that our proposed methods performed well. 2. Comparison with ABC-ER and ABC-Fit2C The comparison between the proposed methods with ABC-ER and ABC-Fit2C in [27] is shown in Table 6. The comparison shows that our proposed methods performed better than all the seven data sets on both accuracy and number of selected features. However, even though ABC-Fit2C chooses slightly fewer features on German and Vehicle data sets than the proposed methods, but still, the proposed methods attained an improved classification accuracy compared to the ABC-Fit2C and ABC-ER. Therefore, the results displayed in Table 6 indicated that the proposed methods can effectively evolve a fewer number of features and yet achieve a better classification performance. 3. Comparison with BAIS The results obtained by the proposed methods with Bayesian and artificial immune system (BAIS) in work [10] is displayed in Table 7. The results show the superiority of the proposed methods on all the five data sets. The proposed methods outperformed the BAIS with nearly 10% of the classification accuracy on Ionosphere and Sonar data sets. Whereas, around 2–3% of improvement was realised on the proposed methods compared to the BAIS on the Mushroom, WineEW and WBCD data sets. Moreover, fewer subsets of features were selected in the proposed method than the BAIS. Therefore, both in terms of selected features and the classification accuracy, the proposed methods outperformed the BAIS in all aspects.

5.5 Comparisons Between BCOA and COA Comparing the performance of COA and BCOA for adopted or combined objectives as shown in tables (Tables 4 and 3) for both categorical and continuous data sets, one can observe that BCOA outperformed COA in terms of number of selected features, accuracy and best accuracy for all the proposed methods.

WineEW

Ionosphere

German

Zoo

Approach

BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S

Datasets

8 8 8 7 8 7 9 9.18 9 8 9.18 8 12.5 12.58 12.1 11.5 11.92 11.2 12.56 12.58 12.12 11.85 12.05 10.65

Ave-Size 86.21 95.96 86.21 86.21 95.96 100.00 86.21 97.14 86.21 98.56 97.14 98.56 86.21 72.00 86.21 86.21 72.00 86.21 88.82 93.33 92.46 94.26 93.33 98.25

Best-Acc

86.21 96.25 95.50 96.25 86.21 95.50 86.21 72.48 69.41 74.25 75.45 72.00 75.70 86.21 88.40 86.21 86.21 91.43 86.21

96.75 97.25

96.75

Ave- Acc

LungCancer

WBCD

Vehicle

Australian

Datasets

Table 5 Comparison of (BCOA-FS, COA-FS, BCOA-2S and COA-2S) with ErFS and 2SFS BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S

Approach 3.42 3.88 3.42 3.32 3.42 3.32 9.1 9.52 9.1 9 8.65 9 13.41 13.42 13.52 13.21 5 12.45 27 27.35 27.6 25.2 27.38 25

Ave-Size 89.25 87.44 89.25 90.56 87.44 90.58 89.56 87.01 89.56 91.25 87.01 91.56 95.21 94.74 94.75 98.22 94.74 100.00 90.65 80.00 90.31 92.25 90.00 95.50

Best-Acc

(continued)

86.25 85.44 86.25 87.58 84.24 87.58 85.22 87.01 85.22 91.25 84.95 91.56 91.28 93.39 92.45 98.22 94.74 100.00 88.65 72.00 89.56 92.25 80.00 95.50

Ave- Acc

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BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S BCOA-FS ErFS COA-FS COA-2S 2SFS BCOA-2S

Hillvalyey

Madelon

Approach

Datasets

Table 5 (continued)

45.25 47.32 46.12 44.6 47.04 42.4 255.5 258.1 259.2 250.6 256.48 248.52

Ave-Size 68.52 61.81 64.26 70.65 61.81 74.25 80.00 79.49 79.89 81.45 79.36 83.45

Best-Acc 65.22 57.54 59.62 68.56 57.57 70.35 80.00 76.55 77.47 79.45 76.52 81.65

Ave- Acc Musk1(Clean1)

Datasets ErFS BCOA-FS COA-FS COA-2S 2SFS BCOA-2S

Approach 86.48 84.25 85.6 41.25 85.58 50.1

Ave-Size

88.81 88.88 88.81 88.97 88.81 90.50

Best-Acc

84.58 86.21 86.21 86.21 88.88 86.21

Ave- Acc

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BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S

Optical

Madelon

Ionosphere

German

Approach

Datasets

30 41.13 32 26.5 37.43 20.45 12.5 10.76 12.1 11.5 9.13 11.2 12.56 12 12.12 11.85 11.53 10.65 255.5 252.46 259.2 250.6 248.03 248.52

Ave-Size 100.00 98.10 100.00 100 98.22 100 86.21 70.17 86.21 86.21 70.01 86.21 88.82 92.12 92.46 94.26 91.74 98.25 80.00 72.91 79.89 81.45 72.20 83.45

Best-Acc

77.47 79.45 – 81.65

98.98 – 98.98 100.00 – 100.00 72.48 – 74.25 75.45 – 75.70 86.21 – 86.21 86.21 – 86.21 80.00

Ave- Acc

Hillvalyey

Musk1(Clean1)

Vehicle

Datasets BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S BCOA-FS ABC-ER COA-FS COA-2S ABC-Fit2C BCOA-2S

Approach

Table 6 Comparison of (BCOA-FS, COA-FS, BCOA-2S and COA-2S) with ABC-ER and ABC-Fit2C 9.1 9.86 9.1 9 7.73 9 86.48 83.03 85.6 41.25 80.56 50.1 45.25 47.63 46.12 44.6 44.96 42.4

Ave-Size 89.56 79.53 89.56 91.25 77.88 91.56 88.81 83.11 88.81 88.97 82.23 90.50 68.52 54.13 64.26 70.65 54.92 74.25

Best-Acc 85.22 – 85.22 91.25 – 91.56 84.58 – 86.21 86.21 – 86.21 65.22 – 59.62 68.56 – 70.35

Ave- Acc

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Sonar

Ionosphere

Approach

BCOA-FS COA-FS COA-2S BAIS BCOA-2S BCOA-FS COA-FS COA-2S BAIS BCOA-2S BCOA-FS COA-FS COA-2S BAIS BCOA-2S

Datasets

WineEW

8 8 7 7.8 7 12.56 12.12 11.85 13.4 10.65 23.42 24.5 21.9 23.6 19.8

Ave-Size 86.21 86.21 86.21 98.40 100.00 88.82 92.46 94.26 91.20 98.25 86.21 86.21 86.21 77.30 86.21

Best-Acc 96.75 96.75 97.25 – 86.21 86.21 86.21 86.21 – 86.21 90.65 86.45 96.58 – 98.20

Ave- Acc

WBCD

Mushroom

Datasets

Table 7 Comparison of (BCOA-FS, COA-FS, BCOA-2S and COA-2S) with BAIS Approach BCOA-FS COA-FS COA-2S BCOA-2S BAIS BCOA-FS COA-FS COA-2S BAIS BCOA-2S

Ave-Size 12.5 13.5 8 7 11.5 13.41 13.52 13.21 14.3 12.45

Best-Acc 98.98 98.98 100.00 100.00 98.10 95.21 94.75 98.22 97.20 100.00

95.65 94.75 98.60 99.80 – 91.28 92.45 98.22 – 100.00

Ave-Acc

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Even though BCOA is a discrete binary version of COA, however, it can be seen that it outperformed COA not only on the categorical or discrete data sets but also on the continuous data sets. Continuous or discrete data sets refer to the data sets that have their class label either as categorical or continuous. Analysis of the computational time also shows that BCOA can complete its evolutionary process within the shortest time than the COA on the majority of the data sets. BCOA is faster than COA in around 10–5% majority of some of the data sets regardless of the continuous or categorical data sets. Meanwhile, this motivates the use of BCOA alone in the multi-objective wrapper-based feature selection. Moreover, this will avoid repetition of similar explanation of BCOA of being the best compared to its COA counterpart.

5.6 Further Discussions The results show that both BCOA-FS and COA-FS can successfully evolve a set of features with better classification performance within a short period. However, as the number of features increase, BCOA-FS perform better than COA-FS, especially on the categorical datasets. Whereas, the COA-FS performed better mostly on the continuous class label dataset. It demonstrates that the continuous version works well on the continuous label datasets. In contrast, the binary version works well on the majority of the datasets and mostly performed better on the categorical datasets. Correspondingly, both BCOA-2S and COA-2S can successfully select the best features with better classification performance than the COA-FS and BCOA-2S on the majority of the datasets. Also, BCOA-2S outperformed COS-2S in most cases due to the use of the two-step evaluation process. The proposed approaches used a β value of [0,1] in the evolution process. However, choosing the most appropriate value is quite a challenge. Because most of the selected features, along with their classification performance, are combined into a single fitness function. Nowadays, FS is considered as a multi-objective optimisation problem and treating the FS in that regards will solve the task much better and obtain the set of nondominated solutions.

6 Conclusions and Future Work This paper disclosed the first study on wrapper-based feature selection using COA and BCOA. Four wrapper-based feature selections are presented. Both BCOA and COA were adopted and used as a wrapper based in the evolutionary process. Then a two-step fitness function was proposed, whereby the new classification performance obtained in the first step is combined with the number of selected features in the second step. The results obtained showed that the proposed methods performed well compared to the previous work. However, combining the two aims of the feature

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selection into a single fitness function cannot solve the problem better, and there will be some redundancy still among the number of selected features. Hence there is need for multi-objective feature selection that treats both numbers of selected features and classification performance simultaneously. On the other hand, COA, especially its binary version, has performed well for FS because (1. COA representation is suitable for FS problems. The habitats in COA is Nvar -dimensional array representing the current living position of cuckoos, which looks like the way candidate solutions are represented in the FS problem. In this case, the size of the dimensionality is the number of features. The values in any dimension/habitat display whether a feature is chosen or otherwise. (2. The search space in FS problems is too large and mostly get stuck in local optima in most of the existing methods. As such, there is a need for a global search technique. These ECs are well-known for solving problems that do not have a solution; they are robust to dynamic changes and have broad applicability [14, 49, 50]. COA is an EC; precisely an evolutionary algorithm based that has effective and efficient search operators that can search for large space to discover the optimum otherwise nearby optimum solution [14]. Acknowledgements The authors want to thank Universiti Sains Malaysia (USM) for supporting and backing the research via its Research University Grant (RUI) (1001/PKOMP/8014084) along with Woosong University, Korea.

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Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier Kayode S. Adewole , Muiz O. Raheem, Oluwakemi C. Abikoye, Adeleke R. Ajiboye, Tinuke O. Oladele, Muhammed K. Jimoh, and Dayo R. Aremu Abstract Within the multitude of security challenges facing the online community, malicious websites play a critical role in today’s cybersecurity threats. Malicious URLs can be delivered to users via emails, text messages, pop-ups or advertisements. To recognize these malicious websites, blacklisting services have been created by the web security community. This method has been proven to be inefficient. This chapter proposed meta-heuristic optimization method for malicious URLs detection based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) as well as random forest (RF) were used for classification of phishing web pages. Experimental results show that WOA reduced model complexity with comparable classification results without feature subset selection. RF classifier outperforms SVM based on the evaluation conducted. RF model without feature selection produced accuracy and ROC of 0.972 and 0.993, respectively, while RF model that is based on WOA optimization algorithm produced accuracy of 0.944 and ROC of 0.987. Hence, in view of the experiments conducted using two well-known phishing datasets, this research shows that WOA can produce promising results for phishing URLs detection task. K. S. Adewole (B) · M. O. Raheem · O. C. Abikoye · A. R. Ajiboye · T. O. Oladele · D. R. Aremu Department of Computer Science, University of Ilorin, Ilorin, Nigeria e-mail: [email protected] M. O. Raheem e-mail: [email protected] O. C. Abikoye e-mail: [email protected] A. R. Ajiboye e-mail: [email protected] T. O. Oladele e-mail: [email protected] D. R. Aremu e-mail: [email protected] M. K. Jimoh Department of Educational Technology, University of Ilorin, Ilorin, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_7

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Keywords Phishing detection · Meta-heuristic · Genetic algorithm · Wolf optimization · Machine learning

1 Introduction The significance of the world wide web (WWW) has constantly been expanding. These days the internet is an integral part of everybody’s daily activities and has contributed tremendously in information sharing across the globe. Technology grows with huge speed, which makes the user to utilize it in a smarter way. As technology advances, motives for usage also grow vastly. Numerous attacks are exhibited over the network; one of them is phishing in which the attacker impersonates himself as genuine to hijack the user’s credentials. Unfortunately, technological advancement combined with new sophisticated attack has increased tremendously and the number of victims is growing [1]. Such attack incorporates rogue websites that sell fake products, monetary extortion by deceiving users into uncovering delicate information, which inevitably leads to stealing of money or personality, or notwithstanding introducing malicious software on the network. There exist a broad range of methods for carrying out such assaults; for instance, social engineering, drive-by exploits, watering hole, phishing, SQL injection, man-in-the-middle, loss/theft of gadgets, denial of service and many others [2]. To engage in phishing attack, attacker can disguise uniform resource locator (URL) to appear as legitimate to the visitors of the website. Resources and other documents on the web are accessed through URLs. URL is the worldwide location with two major elements: the protocol identifier and the resource name. The protocol identifier represents the protocol used to access the web resource, while resource name depicts the domain name or IP address where the web resource is located. These two components are separated with a colon and double forward slashes, as shown in Fig. 1. Web Browser Host name

http://www.google.com.sg/webhp?hl=zh-CN Protocol

Top-level Domain Primary Domain

Path

http://218.253.0.77/webhp?hl=zh-CN

Fig. 1 Sample URL [2]

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Malicious URLs have turned into a typical means to encourage cybercrimes, which include drive-by-download and spamming. Numerous attackers endeavour to utilize malicious sites to distribute malicious projects or compromise users’ data. According to Kaspersky Lab, there has been increase in browser-based attacks in 2012 ranging between 946,393,693 and 1,595,587,670. However, 87.36% of these attacks were utilized maliciously. The Anti-Phishing Working Group additionally states that phishing attacks that make use of malicious links expanded from 93,462 to 123,486 during the second half of the year 2012. It has been observed by Lin et al. [3] that out of the millions of URLs utilized every day, malicious URLs are shortlived so as to avoid blacklist-based detection. Early detection method for malicious URLs utilized blacklist-based approach. This approach depends heavily on repositories of already categorized web pages. Blacklist-based method has the problem of generality owing to the fact that any URL that is not listed in the repositories might not be detected [4]. Conversely, machine learning methods have played significant roles and have also been used to build intelligent frameworks that distinguish malicious web pages from genuine ones. For example, Gupta [5] used pattern matching algorithm with word segmentation to pinpoint malicious web pages. Naïve Bayes and sequential minimal optimization (SMO) approach have been studied in the work of Aydin and Baykal [6]. Li et al. [7] proposed supervised boosting decision tree approach to detect phishing URL. Wang et al. [8] proposed a hybrid classification approach based on static and dynamic analyses for malicious website detection and Adewole et al. [9] discovered the possibility of phishing detection using hybrid rulebased method through a combination of two commonly used rule induction algorithms: JRip and Projective Adaptive Resonance Theory (PART). Notwithstanding, numerous researches have employed machine learning to deploy intelligent frameworks for malicious URLs detection, however, employing meta-heuristics approach with classification techniques to build a more accurate system to efficiently detect malicious web pages still remains an open research issue. Therefore, this chapter investigates the performance of two meta-heuristic optimization algorithms based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) and random forest (RF) algorithms have been used as classifiers to evaluate the performance of the features selected from the two meta-heuristic algorithms. These algorithms were selected in this study based on their outstanding performances as reported in the literatures [6, 15, 16, 20]. The subsequent sections are arranged as follows: Sect. 2 focuses on related studies on malicious URLs detection, Sect. 3 discusses the methodology of the proposed framework and Sect. 4 presents the results of the various experiments conducted in this study. The last section, Sect. 5 concludes the chapter and presents the future direction of the study.

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2 Related Works Several studies have been conducted to detect malicious websites. This section discusses related studies on malicious URLs detection. For instance, Xuan and Yongzhen [10] developed malicious URLs detection system using two-dimensional barcodes and hash function. In their approach, the researchers extracted eigenvalues of malicious and benign links. The system generates black and white list library for the URLs extracted. Based on the match rules produced, the authors presented safety tips for users in accordance with these rules. Lexicon-based approach has been studied in the literature to detect malicious websites. For instance, Darling et al. [11] employed lexical analysis of URLs to categorize websites according to the level of maliciousness. The main idea is to identify the classification model based on lexical analysis that could be employed in real time to detect malicious URLs. In addition, Lee and Kim [12] focused on detection of malicious URLs in a twitter data stream. Authors concentrated on discovering frequently distributed URLs to uncover the deviousness of associated link redirect chains. Tweets from Twitter timeline were experimented upon to develop a classification model for phishing URLs detection. Experimental result reveals that the classification approach was capable of accurately identifying suspicious links in a tweet. Gupta [5] employed algorithm for pattern matching on word segmentation to pinpoint phishing URL. Naïve Bayes and SMO approach have been studied in the work of Aydin and Baykal [6]. Li et al. [7] developed supervised boosting decision tree method to detect phishing URL. Wang et al. [8] proposed a hybrid classification approach based on static and dynamic analyses for malicious website detection. Also, models for spam message and spam account detection on Twitter have been studied extensively in the literature [13]. In addition, Bhardwaj, Sharma and Pandit [14] proposed artificial bee colony algorithm for identifying malicious links on the web. Furthermore, a study carried out by Adewole et al. [9] applied a hybrid rule-based technique to identify malicious URLs. The research showed that PART algorithm is superior to JRip when deployed for phishing URLs detection task. Thus, their study concluded that the hybrid rule induction method that combined the rules generated from the two induction algorithms performed better than both PART and JRip in terms of accuracy. Babagoli et al. [18] proposed a method for phishing website detection that utilizes meta-heuristic-based nonlinear regression algorithm coupled with feature selection technique. The authors evaluated the proposed approach using dataset that comprises 11055 phishing and legitimate web pages. Twenty (20) features were focused on to build the phishing detection system. Sohrabi and Karimi [19] proposed spam comments detection model from Facebook social network. Sahingoz et al. [20] proposed natural language processing (NLP) features to train classification algorithm for real-time anti-phishing detection system. Seven classification algorithms were used to evaluate the performance of the NLP-based features. The study observed that RF algorithm produced the best result with an accuracy of 97.98% for phishing URLs detection.

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Although a number of machine learning models have been investigated to detect phishing URLs, however, investigation based on WOA and GA for feature optimization with SVM and RF classifiers is the main focus of this research.

3 Methodology

Datasets

This section discusses the methodology employed to build the proposed model for phishing URL detection. It describes the approach for data collection as well as the optimization algorithms that were used for features optimization. Summarily, Fig. 2

Preprocessing

GA Model

WOA Model

SVM/RF

SVM/RF

GA+SVM/ GA+RF Evaluation

WOA+SVM/ WOA+RF Evaluation

Best Model Fig. 2 Proposed framework for phishing URLs detection

SVM/RF

SVM/RF Evaluation

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Table 1 Description of the two datasets utilized in the study Dataset name

No. of features

Attributes characteristics

No. of instances

Class distribution

Dataset1

10

Integer

1,353

Malicious (702), genuine (548), suspicious(103)

Dataset2

30

Integer

11,055

Malicious (4898), genuine (6157)

shows the proposed framework in this study to detect malicious web pages. The subsequent sections highlight the components of the proposed framework and the techniques deployed to achieve them.

3.1 Method of Data Collection and Preparation This study examined two publicly available datasets for malicious URLs detection. The datasets were obtained from UCI machine learning repository. First dataset, henceforth referred to as Dataset1, is accessible at ‘https://archive.ics.uci.edu/ml/ machine-learning-databases/00379/’, which has a total of 1,353 URLs instances. This dataset has ten (10) attributes for analysis. The second dataset, henceforth referred to as Dataset2, is accessible at ‘https://archive.ics.uci.edu/ml/machine-learning-dat abases/00327/’, which has a total of 11,055 URLs instances. This dataset has 30 attributes for analysis. Table 1 depicts the composition of these datasets. Detailed reports of the various features in the datasets can be found in [9].

3.2 Feature Subset Selection Feature selection, as a data preprocessing approach, has been proven to be powerful and efficient in the preparation of data with high dimensionality for various data mining and machine learning issues. The goals of feature selection include constructing less complicated and more comprehensible models, enhancing machine mining model performance, and preparing clean and understandable data of high quality. In order to select a subset of features of high quality from the two datasets considered in this study, two meta-heuristic optimization algorithms were studied, which are GA and WOA algorithms. The subsequent sections discussed the two algorithms in detail.

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3.3 Meta-Heuristics Algorithms As discussed in the previous sections, this study investigated two meta-heuristic algorithms: GA and WOA based on their optimal performance as testified in the literature. Genetic Algorithm. GA is a meta-heuristic search algorithm inspired by Charles Darwin’s theory of natural evolution. Its operation mimics the natural selection process and the algorithm has gained wider usage for solving optimization problems over the years. GA selects fittest individuals for reproduction so as to yield offspring of the next generation. The algorithm terminates if it does not produce offspring that are significantly different from the previous generation. This means that the population has converged on this iteration and a set of solutions to the problem has been identified. In relation to feature subset selection, GA searches the best combination of features that will provide improved results in the solution space. GA has several stages which are demonstrated in Algorithm 1. Algorithm1: Genetic Algorithm [Start] Generate random population of n chromosomes (appropriate solutions for the problem) [Fitness] Calculate the fitness f(x) of all chromosome x in the population [New population] Generate a new population by reiterating the subsequent phases till the new population is complete [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the more chance to be selected) [Crossover] With a crossover, probability crosses over the parents to form a new offspring (children). If no crossover was achieved, offspring is an exact copy of parents. [Mutation] With a mutation, probability mutates a new offspring at each locus (position in chromosome). [Accepting] Place new offspring in a new population [Replace] Use new generated population for a further run of algorithm [Test] If the end condition is satisfied, stop, and return the best solution in current population [Loop] Go to step [Fitness]

Wolf Optimization Algorithm. The wolf optimization algorithm (WOA) is one of the bio-inspired meta-heuristics algorithms based on wolf preying behaviour. One of its distinguishing characteristics is the concurrent possession of individual local search capability as well as flocking movement [15]. Therefore, the individual wolf in WOA searches autonomously by memorizing its particular attribute and unites with its peer when the peer is in a better location. Using this approach, long-range inter-communication amongst the wolves that characterize the searching points for candidate solutions is removed since wolves stalk their target in stillness. Also,

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the swarming behaviour of WOA, unlike most bio-inspired algorithms, is delegated to each individual wolf rather than to a single leader, as opposed particle swarm optimization and Firefly. WOA operates as if there are multiple leaders swarming from different directions to the best solution, instead of one direction for optimum solution by a single flock. WOA is summarized in Algorithm 2. Algorithm 2: Wolf Optimization Algorithm Objective function f(x), x=(x1,x2,…,xd)T Initialize the population of wolves, xi(i=1,2,…,W) Define and initialize parameters: r = radius of the visual range s = step size by which a wolf moves at a time α = velocity factor of wolf pa = a user-defined threshold [0..1], it defines how commonly an enemy appears WHILE (t pa) xi = xi + rand() + v; // escape to a new position. END IF END FOR END WHILE

3.4 Classification In order to separate phishing website from legitimates ones, two classification algorithms were employed based on SVM and random forest (RF). These algorithms have been widely used in security domain and have shown significant classification performance [4, 13]. The classifiers take as input a dataset containing the class of each piece of data instance to enable the computer to learn the pattern that exists among the instances in the dataset. This pattern is then used to predict the class of new data samples. In this study, the target attribute of the Dataset1 is divided into three categories: malicious, suspicious and genuine. In the case of Dataset2, the target attribute is binary in nature, which involves malicious and genuine cases. Therefore, the goal of each classifier is to extract pattern that reveals the specific group or class each data instance is related within a given dataset.

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Support Vector Machine. Support vector machine (SVM) is one of the most popular classifiers that has been employed in several domains. SVM utilizes ‘kernel trick’ approach to solve a nonlinearly separable problem through mapping of points into a higher-dimensional space. SVM solves the issues of overfitting that are inherent in some learning algorithms. The main idea of SVM is to compute the optimal separating hyperplane between the classes in the dataset by maximizing the margin between the classes’ closest points. The maximum margin hyperplane is the one that provides the highest separation between the classes considered. In two-dimensional space, this hyperplane is a line that divides a plane into two distinctive parts corresponding to the classes in a binary classification task. SVM can solve both classification and regression tasks and can handle several types of variables. The separating hyperplanes can be defined as: wi xi + b ≥ +1, when yi = +1

(1)

wi xi + b ≤ −1, when yi = −1

(2)

where w is the weight, x is the input, y is the output and b is bias. Random Forest. Random forest (RF) is a type of decision tree algorithms that are based on ensemble technique. RF produces an ensemble of classifiers based on different decision trees using random feature selection and bagging technique during the training phase. The decision tree generates two categories of nodes, namely, the leaf node labelled as a class and the interior node represented with a feature. During training phase, a diverse subset of training samples is chosen with a replacement to train each decision tree. Entropy is applied to compute the information gain contributed by each feature. Let D represents the dataset with the labelled instances  and C as the class such that C = C1 , C2 , C3 , . . . , C j , where j is the number of classes considered. In this study, the value of j is set to 2 or 3 depending on the specific dataset used as earlier discussed. Thus, the information needed to identify the class of an instance in the dataset D is denoted as Info(D) = Entropy(P), where P is the class probability distribution such that:  P=

|C j| |C1| |C2| |C3| , , ,..., |D| |D| |D| |D|

 (3)

By partitioning D based on the value of a feature F according to subsets {D1 , D2 , D3 , . . . , Dn }, Info(F,D) according to F is computed as: Info(F, D) =

n  |Di | i=1

|D|

Info(Di )

(4)

The corresponding information gain after obtaining the value of F is computed as:

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Gain(F, D) = Info(D) − Info(F, D)

(5)

Then the GainRatio is defined as: GainRatio(F, D) =

Gain(F, D) SplitInfo(F, D)

(6)

where SplitInfo(F, D) denotes the information due to the splitting of D according to the feature F. Random forest uses the majority voting of all the individual decisions to obtain the final decision of the classifier [1].

3.5 Cross-Validation Cross-validation is a statistical technique that divides data into two parts: one used to train a model and the other used to validate the model. This method is to evaluate and compare learning algorithms [17]. Cross-validation process uses a single parameter called k. The parameter represents the number of partitions in which a given dataset can be divided. Based on this, the process is frequently called k-fold cross-validation. For instance, k = 10 becomes 10-fold cross-validation. In this study, 10-fold crossvalidation method is used to train the proposed models because this approach has been widely accepted in the literature to avoid model overfitting and to produce better transparent model for phishing web page detection.

3.6 Evaluation Metric Evaluation metrics are the methods used in determining the performance of machine learning models. For this research, the models were evaluated using parameters such as accuracy, sensitivity (recall), specificity, precision, F-measure and receiver operating characteristics (ROC). These metrics are briefly discussed as follows: i.

Accuracy: This is the fraction of predictions that the model gets right. Mathematically, accuracy can be calculated by: Accuracy =

ii.

t p + tn t p + tn + f p + f n

(7)

Sensitivity: Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive or (true positive). Sensitivity is also termed as Recall. Therefore, in this case, it refers to the model ability to detect phishing URL correctly. Mathematically, sensitivity is calculated by:

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Sensitivity = iii.

(8)

tn tn + f p

(9)

Precision: This metric represents the proportion of the data instances that the model predicts to be relevant which is truly relevant. It is the number of true positives divided by the number of true positives plus the number of false positives. It is calculated mathematically by: Precision =

v.

t p+ fn

Specificity: This is defined as the proportion of actual negatives, which got predicted as the negative (or true negative). In essence, specificity measures the model ability to correctly detect web pages that are actually legitimate (i.e. not phishing web pages). It can be calculated mathematically by: Specificity =

iv.

tp

tp tp + f p

(10)

F-measure: This is the weighted harmonic mean of precision and recall. It is calculated as follows: F − measure =

2P R (P + R)

(11)

Considering the equations above, true positive (t p ) is the number of phishing URLs that were correctly identified as phishing, false positive (f P ) is the number of legitimate URLs that were incorrectly detected as phishing. True negative (t n ) is the number of legitimate URLs that were correctly identified as legitimate, while false negative (f n ) is the number of phishing URLs that were incorrectly detected as legitimate.

4 Results and Discussion This section explains the process employed in detecting malicious URLs using selected meta-heuristic algorithms for feature selection and SVM and RF for classification. It discusses the experiments performed, simulation tool as well as the result analysis. To evaluate the performance of each of the selected meta-heuristics algorithms, various experiments were carried out using Dataset1 and Dataset2. The experiment was performed on Windows 10 operating system, having a random-access memory (RAM) of 4 GB and 2.50 GHz Intel Core i5 CPU with 500 GB hard disk. Finally, cross-validation using 10-fold was employed to assess the performance of the selected meta-heuristic algorithms on the two phishing datasets.

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4.1 Modelling and Interpretation The experiments were conducted using WEKA 3.8.2 version, which is a simulation tool with different machine learning algorithms for predictive tasks. It has the capability for data preparation, classification, regression, clustering, associate rule mining and visualization. WEKA is a popular tool for data mining. It is an open-source and freely available platform-independent software. It has flexible facilities for scripting experiments. Classification performance based on SVM. Table 2 presents the classification results of SVM based on all the features in the two datasets that were analysed in this study. From this result, SVM is able to achieve performance accuracy of 86.60% and 93.8% for Dataset1 and Dataset2, respectively. The false positive was 0.109 for Dataset1 and 0.066 for Dataset2. The results of other metrics considered were also promising, which shows the significance of the model for distinguishing phishing web pages from legitimate ones. Classification performance based on RF. The results obtained based on RF algorithm without feature selection is summarized in Table 3. RF produced better results across the evaluation metrics used in this study. For instance, the accuracy of RF algorithm is 89.4% on Dataset1 and 97.2% on Dataset2. As shown in the table, ROC of the proposed RF model for two datasets was estimated at 96.3% and 99.3%, respectively. This shows the capability of the proposed framework to effectively detect phishing Table 2 Classification based on SVM only

Dataset1

Dataset2

Accuracy

0.860

0.938

TP rate

0.860

0.938

FP rate

0.109

0.066

Precision

0.843

0.938

Recall

0.860

0.938

F-measure

0.846

0.938

ROC area

0.900

0.936

Dataset1

Dataset2

Accuracy

0.894

0.972

TP rate

0.894

0.972

Table 3 Classification based on RF only

FP rate

0.081

0.031

Precision

0.894

0.972

Recall

0.894

0.972

F-measure

0.894

0.971

ROC area

0.963

0.993

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web pages. In addition, false alarm was reduced across the two phishing datasets. The importance of the RF model to produce relevant results is very significant as demonstrated by the results obtained with the precision metric. Classification performance based on GA and SVM. GA was used for feature selection on Dataset1 and Dataset2, each having ten (10) and thirty (30) features, respectively, as earlier highlighted. GA selected five (5) optimal features for Dataset1 and nine (9) optimal features for Dataset2. With the reduced features for modelling, the proposed framework based on GA was able to achieve very promising results without compromising the model performance as shown in Table 4. Using the minimal number of features as selected from the GA algorithm produced accuracy of 83.7% and 93.3% for Dataset1 and Dataset2, respectively. This result reveals that the proposed approach is able to reduce model complexity while still retain better performance. Classification performance based on GA and RF. Based on the selected features from GA, RF model produced improved results on Dataset2. The model accuracy dropped slightly on Dataset1; however, the ROC result is better than the SVM model with GA selected features. Table 5 summarized the results based on GA and RF algorithms. From this result, an accuracy of 82.3% and 94.4% was obtained on Dataset1 and Dataset2, respectively. This result implied that GA is a good feature subset selection algorithm for developing a model to detect malicious web pages. Table 4 Classification based on GA and SVM

Dataset1

Dataset2

Accuracy

0.837

0.933

TP rate

0.837

0.933

FP rate

0.129

0.072

Precision

0.792

0.933

Recall

0.837

0.933

F-measure

0.811

0.933

ROC area

0.866

0.931

Dataset1

Dataset2

Accuracy

0.823

0.944

TP rate

0.823

0.944

Table 5 Classification based on GA and RF

FP rate

0.135

0.059

Precision

0.804

0.944

Recall

0.823

0.944

F-measure

0.811

0.944

ROC area

0.935

0.986

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Table 6 Classification based on WOA and SVM

Table 7 Classification based on WSA + RF

Dataset1

Dataset2

Accuracy

0.837

0.933

TP rate

0.837

0.933

FP rate

0.129

0.072

Precision

0.792

0.933

Recall

0.837

0.933

F-measure

0.811

0.933

ROC area

0.866

0.931

Dataset1

Dataset2

Accuracy

0.823

0.944

TP rate

0.823

0.944

FP rate

0.135

0.059

Precision

0.804

0.944

Recall

0.823

0.944

F-measure

0.811

0.944

ROC area

0.935

0.987

Classification performance based on WOA and SVM. Wolf optimization algorithm (WOA) was used for feature subset selection on both datasets. The results were similar to those obtained in GA. WOA also selected five (5) optimal features for Dataset1 and nine (9) attributes for Dataset2. The results are summarized in Table 6. Classification performance based on WOA and RF. Since the number of features selected by the two meta-heuristic algorithms considered in this study is the same, WOA with RF algorithm produced similar results when compared with GA with RF algorithm. These results are also summarized in Table 7 with an accuracy of 82.3% and 94.4% for Dataset1 and Dataset2, respectively. These results show that the two meta-heuristic algorithms considered in this study have produced promising results comparable to the results obtained without feature subset selection. This approach reduced the model complexity and guaranteed an improved prediction time when deployed in a real-life environment.

4.2 Models Comparison Based on SVM as a Classifier Figures 3 and 4 show the comparison of the results of the models based on SVM algorithm for phishing URLs detection on Dataset1 and Dataset2, respectively. The meta-heuristic algorithms have really demonstrated comparable results considering the results obtained with the evaluation metrics.

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1 0.8 Weighted avg TP 0.6

Weighted avg FP Precision

0.4

Recall F-measure

0.2 0 SVM

GA+SVM

WSA+SVM

Fig. 3 Comparison of the SVM models based on Dataset1

1 0.8 Weighted avg TP 0.6

Weighted avg FP Precision

0.4

Recall F-Measure

0.2 0 SVM

GA+SVM

WSA+SVM

Fig. 4 Comparison of the SVM models based on Dataset2

Furthermore, the time taken by each model based on SVM classifier with and without feature subset selection revealed that the models based on meta-heuristic algorithms produced in complexity as demonstrated by their training time in seconds. However, the WOA and SVM model took the least time, having 0.16 and 8.72 s for Dataset1 and Dataset2, as shown in Figs. 5 and 6, respectively.

4.3 Models Comparison Based on RF as a Classifier Similarly, Figs. 7 and 8 show the performance of the different RF models on Dataset1 and Dataset2, respectively. As shown in these figures, RF models based on the two meta-heuristic algorithms produced comparable results when compared with the RF model without feature subset selection.

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Time(secs)

0.2 0.1 0 SVM

GA+SVM

WolfSearch+SVM

Fig. 5 Comparison of time taken for Dataset1 with SVM models 24 20 16 12

Time(secs)

8 4 0 SVM

GA+SVM

Wolf+SVM

Fig. 6 Comparison of time taken for Dataset2 with SVM models 1 0.8 Weighted avg TP 0.6

Weighted avg FP Precision

0.4

Recall F-Measure

0.2 0 RF

GA+RF

Fig. 7 RF models comparison based on Dataset1

WSA+RF

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1 0.8 Weighted avg TP 0.6

Weighted avg FP Precision

0.4

Recall F-Measure

0.2 0 RF

GA+RF

WSA+RF

Fig. 8 RF models comparison based on Dataset2

0.18 0.15 0.12 0.09

Time(secs)

0.06 0.03 0 RF

GA+RF

WSA+RF

Fig. 9 Comparison of time taken for Dataset1 with RF models

Similarly, it was observed that the time taken by the WOA algorithm on the two datasets was reduced when compared with GA and RF model without feature subset selection (see Figs. 9 and 10). This finding reveals that WOA algorithm is a better candidate to produce a model with less complexity when feature dimensionality reduction is being considered to build a prototype of phishing URLs detection system. WOA and RF model took the least time, having 0.02 and 0.08 s for Dataset1 and Dataset2, respectively.

4.4 Comparison with Existing Models This section shows the comparison of the results of the proposed models in this study with the existing model that have been developed in the literature. Table 8

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0.4 0.3 0.2

Time(secs)

0.1 0

RF

GA+RF

WSA+RF

Fig. 10 Comparison of time taken for Dataset2 with RF models

Table 8 Baseline comparison with the existing model based on Dataset2 Approaches Models

TPR

FPR

Accuracy Precision Recall F-Measure ROC

Proposed

RF

0.972 0.031 0.972

0.972

0.972

0.971

0.993

Proposed

WOA + RF 0.944 0.059 0.944

0.944

0.944

0.944

0.987

Aydin and Baykal [6]

SMO

0.938

0.938

0.938

0.936

0.938 0.066 0.938

summarizes the results of the various models under consideration. As discussed in the previous section, Naïve Bayes and SMO approach were studied in the work of [6]. From this table, the proposed models were able to outperform the state-of-theart producing promising results across the different evaluation metrics considered in this study. In order to ensure objectivity in the models’ comparison in this section, the SMO experiment in [6] was conducted and the results reported in Table 8 for comparison with the proposed models in this study.

5 Conclusion In this study, the performance of two meta-heuristic algorithms, GA and WOA, was examined for feature subset selection towards identifying malicious web pages. Due to the high exploration capability of features selection of the two meta-heuristic algorithms, they demonstrated promising results when compared with models without feature subset selection. SVM and RF classifiers were considered to develop effective classification models for the proposed framework. From the results obtained, the models based on RF outperformed SVM model using performance metrics such as accuracy, sensitivity, specificity, recall, precision, F-measure and ROC. The results of RF and WOA with RF models were compared with existing state-of-the-art model,

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and the outcome revealed that the proposed models in this study gave better performance when compared with the state-of-the-art. Furthermore, this study observed that WOA meta-heuristic optimization algorithm gave the least running time and reduced model complexity when compared with other models developed in this study. Although the performance of the models without feature subset selection is better, however, models based on meta-heuristic optimization were able to reduce complexity with a slight reduction in the accuracy. In future, the authors intend to investigate other feature categories that may improve the performance of the models for phishing detection. In addition, other feature selection methods need to be investigated.

References 1. Priya, R.: An ideal approach for detection of phishing attacks using naïve bayes classifier. Int. J. Comput. Trends Technol. (Technology) 40, 84–87 (2016) 2. Sahoo, D., Liu, C., Hoi, S.C.: Malicious URL detection using machine learning: a survey, arXiv preprint arXiv:1701.07179 (2017) 3. Lin, M.-S., Chiu, C.-Y., Lee, Y.-J., Pao, H.-K.: Malicious URL filtering—A big data application. In: 2013 IEEE International Conference on Big Data, pp. 589–596 (2013) 4. Vanhoenshoven, F., Nápoles, G., Falcon, R., Vanhoof, K., Köppen, M.: Detecting malicious URLs using machine learning techniques. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016) 5. Gupta, S.: Efficient malicious domain detection using word segmentation and BM pattern matching. In: 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–6 (2016) 6. Aydin, M., Baykal, N.: Feature extraction and classification phishing websites based on URL. In: 2015 IEEE Conference on Communications and Network Security (CNS), pp. 769–770 (2015) 7. Li, Y., Yang, Z., Chen, X., Yuan, H., Liu, W.: A stacking model using URL and HTML features for phishing webpage detection. Future Gener. Comput. Syst. 94, 27–39 (2019) 8. Wang, R., Zhu, Y., Tan, J., Zhou, B.: Detection of malicious web pages based on hybrid analysis. J. Inform. Secur. Appl. 35, 68–74 (2017) 9. Adewole, K.S., Akintola, A.G., Salihu, S.A., Faruk, N., Jimoh, R.G.: Hybrid rule-based model for phishing URLs detection. In: International Conference for Emerging Technologies in Computing, pp. 119–135 (2019) 10. Xuan, J., Yongzhen, L.: The detection method for two-dimensional barcode malicious URLs based on the hash function. In: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 702–705 (2016) 11. Darling, M., Heileman, G., Gressel, G., Ashok, A., Poornachandran, P.: A lexical approach for classifying malicious URLs. In: 2015 international conference on high performance computing & simulation (HPCS), pp. 195–202 (2015) 12. Lee, S., Kim, J.: Warningbird: A near real-time detection system for suspicious urls in twitter stream. IEEE transactions on dependable and secure Comput. 10, 183–195 (2013) 13. Adewole, K.S., Han, T., Wu, W., Song, H., Sangaiah,A.K.: Twitter spam account detection based on clustering and classification methods. J. Supercomput., 1–36 (2018) 14. Bhardwaj, T., Sharma, T.K., Pandit, M.R.: Social engineering prevention by detecting malicious URLs using artificial bee colony algorithm. In: Proceedings of the Third International Conference on Soft Computing for Problem Solving, pp. 355–363 (2014)

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15. Tang, R., Fong, R., Yang, X.-S., Deb, S.: Wolf search algorithm with ephemeral memory. In: Seventh International Conference on Digital Information Management (ICDIM 2012), pp. 165–172 (2012) 16. Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secure Comput. 9(6), 811–824 (2012) 17. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. Encyclopedia of database systems, pp. 532–5382 (2009) 18. Babagoli, M., Aghababa, M.P., Solouk, V.: Heuristic nonlinear regression strategy for detecting phishing websites. Soft. Comput. 23, 4315–4327 (2019) 19. Sohrabi, M.K., Karimi, F.: A feature selection approach to detect spam in the Facebook social network. Arabian J. Sci. Eng. 43, 949–958 (2018) 20. Sahingoz, O.K., Buber, E., Demir, O., Diri, B.: Machine learning based phishing detection from URLs. Expert Syst. Appl. 117, 345–357 (2019)

Improved Cloud-Based N-Primes Model for Symmetric-Based Fully Homomorphic Encryption Using Residue Number System K. J. Muhammed , R. M. Isiaka, A. W. Asaju-Gbolagade, K. S. Adewole, and K. A. Gbolagade Abstract Encryption schemes that allow computation to be performed on an encrypted data are required in modern real-world applications. This technology is a necessity for cloud computing, processing resources and share storage, in order to preserve integrity and privacy of data. The existing partial and fully homomorphic encryption (PHE and FHE) for both asymmetric and symmetric approaches are still suffering from efficiency in terms of encryption execution time and very large ciphertext file produced at the end of encryption. In this paper, we consider symmetric approaches and focus on overcoming the drawbacks of N-prime model using residue number system (RNS). The experimental results obtained show that the proposed RNS-based N-prime model for symmetric-based FHE improves the system latency and reduces the ciphertext file expansion by approximately 72% as compared to the existing N-prime model. The proposed improved N-prime model is guaranteed to provide optimum performance and reliable solution for securing integrity and privacy of user’s data in the cloud. Keywords Homomorphic encryption · Fully homomorphic encryption · Residue number system · Cloud-based · N-prime model · RNS-based N-prime model · Symmetric encryption

K. J. Muhammed (B) Department of Educational Technology, University of Ilorin, Ilorin, Nigeria e-mail: [email protected] R. M. Isiaka · K. A. Gbolagade Department of Computer Science, Kwara State University, Malete, Nigeria e-mail: [email protected] K. A. Gbolagade e-mail: [email protected] A. W. Asaju-Gbolagade · K. S. Adewole Department of Computer Science, University of Ilorin, Ilorin, Nigeria e-mail: [email protected] K. S. Adewole e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_8

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1 Introduction Nowadays, there is a growing interest on cloud technology globally, which informs the efforts by many companies toward implementation of cloud technologies in their business processes. Cloud technology with its own unique feature comprises cloud services, storage, computing, grid computing and distributed computing, among others. Since the idea of cloud technology was projected, there is perpetual attention for research across the world. It has become the hottest research area in the domain of information technology which has been realized as unital of the technology that postures the next-generation computing revolution [1]. Cloud computing offers many opportunities to enterprises for their customers to access and operate computing services as “pay-as-you-go”. Customers saddle with responsibilities of batch processing and web applications explored cloud computing model as an efficient alternative way to acquiring and managing private data centers [2]. Cloud computing as described by US National Institute of Standards and Technology (NIST) as a model that allows and provides convenient ways of accessing unlimited pool of shared resources such as on-demand network access, servers, applications, storage and services categorized as software and hardware that can be rapidly made available to the client as soon as requested through the internet, these resources can be reviewed upward and downward automatically based on client’s demand. Thus, cloud computing has five essential characteristics, three service models and four deployment models such as on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service, software-asa-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) and private cloud, public cloud, community cloud and hybrid cloud, respectively, as shown in Fig. 1 [1, 3]. The quick move toward adoption of cloud computing has raised critical fears on an ultimate fact for the success of information systems, communication, virtualization, data availability and integrity, public auditing, scientific application and information security. Despite the demand of businesses and individual organizations to transfer their data to the cloud, security in terms of data integrity and confidentiality, trust in

Private

Software as a Service (SaaS)

Public

Hybrid

Community

Platform as a Service (PaaS)

Infrastructure as a Service (IaaS)

Fig. 1 Cloud solutions based on the system’s deployment and service model [1]

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terms of data accessibility and availability, and privacy issue have remained as the challenges for embracing cloud computing [4]. Thus, security anxieties arise when transforming data from locally owned storage to third parties cloud service provider’s (CSP) storage. It is expected of CSP to encrypt user’s data before storing it on their cloud server. Several cryptographic schemes such as encryption and decryption, key exchange and steganography have been used to address the security challenges facing the adoption of cloud computing. Encryption means conversion of plain text to ciphertext, and it can be used in various applications for moving data from one location to another. Encryption algorithms such as DES, AES, RSA, Blow fish and many more are meant for secured storage of data and operations on cloud computing server [5, 6]. The traditional encryption schemes seem to be weak for securing the integrity and confidentiality of stored data in the cloud computing server as it requires secret key to be provided by the users so as to decrypt the data prior to the computations, and this threatens the privacy, confidentiality and integrity of stored data. Homomorphic encryption (HE) was introduced to overcome this concern. HE is the process of delegating processing of an encrypted data without having access to the raw data, and produces encrypted result when decrypt result matches the result of the same operations carried out on the raw data. It is considered as one of the most efficient solutions for protecting the confidentiality and user’s information privacy in cloud-based services. Also, HE is grouped as partial homomorphic encryption (PHE) and fully homomorphic encryption (FHE) where PHE ropes either to additive or multiplicative operation, whereas FHE supports both operations and seems to be more secured and efficient than PHE as it poses properties of both operations [7]. The concept of FHE scheme was first introduced by Gentry and Boneh [8] and this was used to address cryptography central open issues. The scheme allows arbitrary functions to be computed over an encrypted data with no reference to decryption key [9]. The development of FHE is a revolutionary advancement, greatly extending the scope of the computations, which can be applied to process encrypted data homomorphically. Gentry [10] presented the first version of FHE scheme using ideal lattices; however, his construction was very complex and impracticable. Since then, FHE has been an interesting research area and several developments that centre on the application, implementation and improvement of FHE schemes have also been studied [11]. There are numerous practical applications of FHE in the real world such as buyer privacy in publicity, medical applications (genomics and health), national security, private prediction, private learning, education, monetary privacy and forensic image recognition, among others [12–14]. There are three major problems associated with the design and practical implementation of FHE scheme such as security problem, time efficiency problem and cipher expansion problem. However, several researchers [15, 16] have contributed to the improvement of FHE in terms of security that is based on hard mathematical problems, and the cost of breaking such scheme at a given security parameter λ should be at least 2λ. Also, in the last 11 years, progress has been made on time efficiency problem by different researchers such as [17–21], and the proposed schemes were categorized into three generations: FHE based on ideal lattices; the second generation

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is based on learning with error (LWE) and ring learning with error (RLWE) problems and the third generation is GSW which is also based on RLWE. However, each of these proposed schemes still needs further improvement for it to be efficiently useful in real-world applications. It is highlighted in [7, 17] that cipher expansion for both asymmetric and symmetric FHE schemes are producing very large ciphertext size between hundreds and tens of thousands as compared to original plaintext size. For instance, ciphertext produced in [17] is till 400,000 times that of the original plaintext and likewise in [7]. These show the growth rate of ciphertext size in an existing FHE schemes which call for serious concern and constitute a major problem of the schemes because it will require large bandwidth to transfer such ciphertext through the network. We proposed scheme conversion concept for encryption scheme originally mentioned in [7] as symmetric-based FHE in order to address the issue. In addition, we provide efficient parallelization in encryption process which produces small ciphertext sizes. However, this study focuses on tackling cipher expansion problem on symmetric-based FHE scheme as proposed by [7] using residue number system. The organization of this paper is as follows. Section 2 describes the related studies on homomorphic encryption; Sect. 3 discusses the research methodology of the proposed framework. Section 4 presents results findings and discussion. Section 5 is the conclusion to the study.

2 Related Works HE schemes are classified into three major categories, as shown in Fig. 2 based on supported homomorphism operations and properties which are partial homomorphic encryption (PHE), somewhat homomorphic encryption (SWHE) and fully homomorphic encryption (FHE). This scheme is called PHE when it allows any number of either addition as in [22, 23] or multiplication operation as described

Homomorphic Encryption (HE)

Partial Homomorphic Encryption (PHE)

Additive HE

Multiplicative HE

Somewhat Homomorphic Encryption (SWHE)

Fully Homomorphic Encryption (FHE)

Additive HE and Multiplicative HE with limited number of operation

Additive HE and Multiplicative HE with Unlimited number of operation

Asymmetric

Fig. 2 Categorization of homomorphic encryption

Symmetric

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in [24, 25]. However, SWHE supports both operations but with limited number of times allowed to evaluate the decryption function because of noise growth as illustrated in [8, 26]. Likewise in [27], the authors presented technique based on SWHE that efficiently secures pre-processing model for dishonest majority in multiparty computation over the ring Z2k . Finally, improvement over SWHE is FHE which allows unlimited number of operations for both additive and multiplicative homomorphism to be performed on an encrypted data without increase in noise during the computation. This type of schemes is extensively discussed in [8, 10, 21, 28]. Parallel to conventional cryptography, FHE schemes are also categorized into asymmetric encryption and symmetric encryption. Asymmetric FHE schemes protect the privacy and confidentiality of data by using different pair of keys (public key and private key) to encrypt the data. Recent research on asymmetric-based FHE has been proposed by [10, 17, 19, 20, 23, 28–33]. However, these schemes suffer from very high computational overhead, cipher expansion and time efficiency which make it difficult to practice in real-world application. However, researchers such as [7, 27, 34–41] have proposed symmetric-based FHE schemes due to its strength in low computational overhead and practical consideration for real-world deployments. Moreover, some of these symmetric-based FHE schemes [7, 41–44] still suffer from large cipher expansion and low immunity against attack. This demanded that a robust security resistant and cipher expansion reduction are highly needed for these symmetric-based FHE schemes [40, 45].

3 Research Method This section discusses the research methodology in relation to homomorphic encryption, N-prime model, residue number system and proposed RNS-based N-prime model for symmetric FHE.

3.1 Homomorphic Encryption Just after discovery of RSA cryptosystem, HE was introduced by [24] as privacy homomorphism that protects the privacy, integrity and confidentiality of data. Generally, HE scheme allows computations on an encrypted variable without the need for decryption prior to the computation or the use of secret key [14, 46]. HE scheme contains four algorithms such as key generation (KeyGen), encryption (Enc), evaluation function (Eval(f)) and decryption algorithm (Dec) which can be described as follows: KeyGen: (pk, sk) ← KeyGen(k), choose parameter k and the algorithm will generate pk as public key and sk as private key.

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Enc: c ← Enc(m, pk), where m is the plaintext-message, pk is public key and c is the ciphertext. Dec: m ← Dec(c, sk), where c is the ciphertext, sk is the private key and m is the plaintext message. Eval(f): c* ← Evaluate (f, Enc(m1), Enc(m2), …… Enc(mn)) However, for the scheme to be homomorphic in nature, it must obey the following properties: Dec(c*, sk) = f(m1, m2, ………. mn) Dec(C1 op C2, sk) = M1 op M2 where C1 and C2 are Enc(M1) and Enc(M2), respectively, op is the group operation (addition or multiplication) in the ciphertext and plaintext space, respectively.

3.2 N-Primes Model In [7], an encryption scheme called N-primes model is investigated for constructing a symmetric-based FHE scheme which converts each plaintext character into ASCII code and passes it to the encryption algorithm c = m + r ∗ l ∗ n where ct is the ciphertext, m is the plaintext message and m ∈ [0, L−1], r is the noise added to the ciphertext, l is a prime big integer and n = p1 ∗ p2 ∗ p3 ∗ . . . pi is the multiplication of numerous prime numbers resulting in one ciphertext for each character in the plaintext message. Thus, encryption and decryption are as follows: Key generation Generaten Generatel ∈ Z n where n is a multiplication of various prime numbers (n = P1 ∗ P2 ∗ P3 ∗ . . . Pi ) and l is a big prime integer value Encryption algorithm c = m + r ∗ l ∗ nwherecis the ciphertext, m ∈ [0, L − 1]and r is random number Decryption algorithm m = c mod l

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Proof of homomorphism Additive property m 3 = c1 + c2 = (m 1 + r1 ∗ l ∗ n) + (m 2 + r2 ∗ l ∗ n) = (m 1 + m 2 ) + l ∗ n(r1 + r2 ) since l ∗ n(r1 + r2 )mod l = 0 then m 3 = (m 1 + m 2 ) Multiplicative Property m 3 = c1 ∗ c2 = (m 1 + r1 ∗ l ∗ n) ∗ (m 2 + r2 ∗ l ∗ n)

m 3 = [(m 1 ∗ m 2 + m 1 ∗ r2 ∗ l ∗ n) + {(m 2 ∗ r1 ∗ l ∗ n) + (r1 ∗ l ∗ n)(r2 ∗ l ∗ n)}]

    m 3 = (m 1 ∗ m 2 + m 1 ∗ r2 ∗ l ∗ n) + (m 2 ∗ r1 ∗ l ∗ n) + r1 ∗ r2 ∗ l 2 ∗ n 2

   m 3 = m 1 ∗ m 2 + m 1 ∗ l ∗ r2 ∗ n + r1 ∗ n ∗ m 2 + r1 ∗ r2 ∗ l ∗ n 2

   Since l ∗ r2 ∗ n + r1 ∗ n ∗ m 2 + r1 ∗ r2 ∗ l ∗ n 2 mod l = 0 and multiple of l mod l = 0 then

m3 = m1 ∗ m2 + m1 ∗ 0 m3 = m1 ∗ m2

3.3 Residue Number System The residue number system (RNS) is a non-weighted and carry-free number system that speeds up arithmetic operations by dividing them into smaller parallel operations.

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It is characterized based on its unique properties which include fault tolerance, modularity, parallelism and carry-free operations, among others. RNS is very efficient in terms of handling arithmetic operations (addition, subtraction and multiplication) [47–50]. RNS can as well be described as moduli set of positive pairwise relatively prime numbers (m 1 , m 2 . . . m n ) and the product of all the modulo (M = m1 × m2 × … × mn ) that produces the dynamic range (DR) of such moduli set. Any integer X in the range [0, M – 1] can be uniquely represented by an ordered set of residues (x1 , x2 ,…,xn ). Each residue xi is represented by xi = X mod mi . To perform residue arithmetic operations such as addition or multiplication can be achieved by adding or multiplying corresponding digits relative to the modulus for their position [50]. For a given moduli set {m 1 , m 2 , . . . , m N }, X = {x1 , x2 , . . . , x N } and Y = {y1 , y2 , . . . , y N } such that xi = |X |m i and yi = |Y |m i where i = 1, 2, …, N, then we may define the residue arithmetic operations as follows: X ⊗Y = Z X ⊗ Y = {x1 , x2 , . . . , x N } ⊗ {y1 , y2 , . . . , y N } = {z 1 , z 2 , . . . , z N } = Z where z i = |xi ⊗ yi |m i and ⊗ is residue arithmetic operator (+, *, and −). As an example, with the moduli-set {3, 4, 5}, the representation of fifteen is {0, 3, 0}, that of twenty-three is {2, 3, 3}, and adding the two residue numbers yield {2, 2, 3} which is the representation for thirty-eight and their product is {0, 1, 0} which is three hundred and forty-five in that system.

3.4 Proposed RNS-Based N-Primes Model for Symmetric FHE Based on the N-prime model, RNS-based N-primes model is built without altering its homomorphic properties. The steps are illustrated in Fig. 3 and will be explained below. The plaintext in a ring Zn will be checked if it is numeric and pass it to RNS forward conversion module, otherwise, generate ASCII codes for each of the text characters before passing it to RNS forward conversion module. Key Generation: This produces very big prime integer value as the secret key for the entire scheme and only to be known by owner of data. Noise Generation: This generates a set of random integer r in a ring Zn as the noise to the entire scheme. N-Primes  n Generation: This also generates product of prime numbers npi = i=1 np1 ∗ np2 . . . npn

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Plaintext

No Get ASCII Code

Is Numeric ?

Yes

RNS Forward Conversion

N-primes Conversion {np1, np2, . . . npn}

Noise Conversion {r1, r2, . . . rn}

Key Conversion {k1, k2, . . . kn}

Moduli Set {m1, m2, . . . ., mn}

Plaintext Conversion {p1, p2, . . . Pn}

Key Generation Noise Generation N-Primes Generation

RNS Encryption Block {p1, p2,. . . . ., pn} + {r1, r2, . . . . ., rn} * {k1, k2, . . . . , kn} * {np1, np2, . . . . . , npn} w.r.t. {m1, m2, . . . . , mn}

Ciphertext {c1, c2, . . . . . , cn}

Cloud Exchange Servers

N-prime Cipher = Chines Reminder Theorem (CRT) with respect to Ciphertext {c1, c2, . . . . . , cn} and Moduli Set {m1, m2, . . . ., mn}

N-prime Cipher Mod Secret Key

Plaintext / Result

Fig. 3 Proposed RNS-based N-prime model

Moduli Set: It takes in n as the input and generates set of pairwise relatively prime integers and moduli set {3n − 2, 3n − 1, 3n } as proposed in [45] which will be used throughout this study, due to its large dynamic range. RNS Forward Conversion: It requires five (5) parameters as input argument (plaintext (pi ), secret key (k i ), noise (r i ), n-prime numbers (npi ) and the moduli set (mi ), where i = 1, 2,…, n), convert pi , k i , r i and npi into residues with respect to mi and send out four outputs into RNS encryption block.

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RNS Encryption Block: Based on output received from RNS forward conversion block, the encryption block will perform RNS arithmetic operation in parallel with respect to moduli set mi and produce the ciphertext. Ci pher text{c1 , c2 , ...., cn } ={ p1 , p2 , . . . , pn } + {r1 , r2 , . . . , rn } ∗ {k1 , k2 , . . . , kn } ∗ {np1 , np2 , . . . , npn }w.r.t.{m 1 , m 2 , . . . , m n } However, the ciphertext can then be transmitted to the Cloud as shown in Fig. 3 and possibly, homomorphic bit operations can be carried out at the cloud server using RNS arithmetic operation based on ciphertext residues cx i and cyi with respect to moduli set mi and the result will be sent to decryption block. Decryption Block: The decryption block adopts Chinese Remainder Theorem as a backward conversion algorithm with the use of ciphertext residues (czi) and moduli set mi to obtain N-prime cipher. k M −1 xi X = i mi i=1

M

k where M = Πi=1 m i , Mi = mMi and Mi−1 is the multiplicative inverse of M i with respect to mi . Finally, user will now use the generated secret key together with N-prime ciphertext to get the result of the operation. The following experiments demonstrate the generation of the moduli set, secret key, other corresponding values and to show how these values are used for encryption and ciphertext reduction without loosen homomorphic behavior of the encryption and decryption block.

Experiment 1 Let the big prime number sk = 524287 be the secret key, random numbers r = 687529 as the noise, product of n-primes np = 23 * 73 * 101 = 69579 and moduli set {3n − 2, 3n − 1, 3n }. When n = 23, then moduli set mi = [94143178825, 94143178826, 94143178827], let message m1 = 65 and message m2 = 66, the RNS-forward conversion for each of these parameters with respect to mi are: m1 = [65, 65, 65] m2 = [66, 66, 66] sk = [524287, 524287, 524287] r = [687529, 687529, 687529] np = [69579, 69579, 69579] To calculate the ciphertexts ct1 and ct2.

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ct1 = |m1 + r ∗ sk ∗ np|mi = |[65, 65, 65] + [687529, 687529, 687529] ∗ [524287, 524287, 524287] ∗ [69579, 69579, 69579]|[94143178825,94143178826,94143178827]

ct1 = [31330438157, 31330171748, 31329905339]

ct2 =|m2 + r ∗ sk ∗ np|mi = |[66, 66, 66] + [687529, 687529, 687529] ∗ [524287, 524287, 524287] ∗ [69579, 69579, 69579]|[94143178825,94143178826,94143178827]

ct2 = [31330438158, 31330171749, 31329905340] Proof of Homomorphism Properties Additive Property ct3 = | ct1 + ct2 |mi = |[31330438157, 31330171748, 31329905339] + [31330438158, 31330171749, 31329905340]|[94143178825,94143178826,94143178827]

ct3 = [62660876315, 62660343497, 62659810679] To decrypt ct3, m3 = chinese_remainder(mi , ct3) mod chinese_remainder(mi , sk) m3 = chinese_remainder([94143178825, 94143178826, 94143178827], [62660876315, 62660343497, 62659810679]) mod chinese_remainder([94143178825, 94143178826, 94143178827], [524287, 524287, 524287]) m3 = 131m3 = 131

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m3 = m1 + m2 = 65 + 66 = 131 Multiplicative Property ct3 = | ct1 ∗ ct2 |mi = |[31330438157, 31330171748, 31329905339] ∗ [31330438158, 31330171749, 31329905340]|[94143178825,94143178826,94143178827]

ct3 = [37639680706, 51114847858, 18251522556] To decrypt ct3, m3 = chinese_remainder(mi , ct3) mod chinese_remainder(mi , sk) m3=chinese_remainder([94143178825, 94143178826, 94143178827], [376396807 06, 51114847858, 18251522556]) mod chinese_remainder([94143178825, 94143 178826, 94143178827], [524287, 524287, 524287]) m3 = 4290 m3 = m1 ∗ m2 = 65 ∗ 66 = 4290 Therefore, the proof shows that the proposed model supports both homomorphic addition and multiplication. Experiment 2 The experiment is also carried out on a string plaintext that contains message “RNS based techniques for encryption of CAP in 2020” with secret key k = 6700417, random number r = 345957466 and product of n-prime np = 31 * 53 * 71 = 116653 and moduli set {3n − 2, 3n − 1, 3n }. When n = 15, then moduli set mi = [14348905, 14348906, 14348907]. Thus, the encryption is as follows: [[13889688, 9226958, 7190942], [13889684, 9226954, 7190938], [13889689, 9226959, 7190943], [13889638, 9226908, 7190892], [13889704, 9226974, 7190958], [13889703, 9226973, 7190957], [13889721, 9226991, 7190975], [13889707, 9226977, 7190961], [13889706, 9226976, 7190960], [13889638, 9226908, 7190892], [13889722, 9226992, 7190976], [13889707, 9226977, 7190961], [13889705, 9226975, 7190959], [13889710, 9226980, 7190964], [13889716, 9226986, 7190970], [13889711, 9226981, 7190965], [13889719, 9226989, 7190973], [13889723, 9226993, 7190977], [13889707, 9226977, 7190961], [13889721, 9226991, 7190975], [13889638, 9226908, 7190892], [13889708, 9226978, 7190962], [13889717, 9226987, 7190971], [13889720,

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Table 1 RNS-based N-prime model vs N-prime model File size

Proposed RNS based N-prime model

N-prime model [7]

Encryption time (MS)

Encryption time (MS)

Ciphertext size (Bytes)

Ciphertext size (Bytes)

2 KB

0.99

5341

1.01

18789

4 KB

1.99

10633

2.99

37529

7 KB

4.98

21217

6.97

75009

14 KB

9.97

42385

10.01

149969

28 KB

18.94

84721

39.89

299889

55 KB

30.43

169393

34.90

599729

110 KB

75.76

338737

78.79

1199409

220 KB

161.54

677425

186.53

2398769

440 KB

319.18

1354801

348.02

4797489

880 KB

741.02

2709553

838.71

9594929

3390.83

5419057

5289.16

19189809

2 MB

9226990, 7190974], [13889638, 9226908, 7190892], [13889707, 9226977, 7190961], [13889716, 9226986, 7190970], [13889705, 9226975, 7190959], [13889720, 9226990, 7190974], [13889727, 9226997, 7190981], [13889718, 9226988, 7190972], [13889722, 9226992, 7190976], [13889711, 9226981, 7190965], [13889717, 9226987, 7190971], [13889716, 9226986, 7190970], [13889638, 9226908, 7190892], [13889717, 9226987, 7190971], [13889708, 9226978, 7190962], [13889638, 9226908, 7190892], [13889673, 9226943, 7190927], [13889671, 9226941, 7190925], [13889686, 9226956, 7190940], [13889638, 9226908, 7190892], [13889711, 9226981, 7190965], [13889716, 9226986, 7190970], [13889638, 9226908, 7190892], [13889656, 9226926, 7190910], [13889654, 9226924, 7190908], [13889656, 9226926, 7190910], [13889654, 9226924, 7190908], [13889652, 9226922, 7190906]]. Experiment 3 In experiment 3, small numbers were used such as 3 for secret key, 5 for random number, 2 * 3 * 5 = 30 for product of n-primes and n = 3 to obtain moduli set = [25–27]. These parameters were used to encrypt message of file size 931 Bytes in 0.47 MS and the ciphertext file is shown in Appendix A. The proposed model has also been tested for 11 different file sizes; encryption execution time and ciphertext file size were recorded and compared with original N-prime model as shown in Table 1.

4 Results and Discussion The proposed RNS-based N-prime model and N-prime model implementations are done under Python using Dell Laptop having the following specifications: 64 bits

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Fig. 4 Encryption time

Fig. 5 Ciphertext expansion

Window 10 operating system, Intel® Core™ i9 processor with 2.90 GHz and 32 GB RAM. The execution time and ciphertext expansion are studied for different plaintext size, and the results are shown in Figs. 4 and 5, respectively. Based on the execution time, the proposed RNS-based N-prime model is taking the lowest execution time, while the N-prime model is taking the highest, and this can be interpreted that during proposed RNS-based N-prime model the encryption is done based on residues number system and also all the computations are performed in parallel without carry propagation which makes the proposed model more efficient and practical. Table 2 shows the improvement of proposed RNS-based N-prime model on the N-prime model in terms of execution time. Table 2 Execution time RATIO (RNS-based N-prime model/N-prime model) Plaintext size in kilobytes 2 Execution time ratio

4

7

14

28

55

110

220

440

880

2000

0.98 0.67 0.72 1.00 0.48 0.87 0.96 0.87 0.92 0.88 0.64

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Table 3 Ciphertext percentage reduction in N-prime model and RNS-based N-prime model Plaintext size 2 in kilobytes Percentage reduction (%)

4

7

14

28

55

110

220

440

880

2000

71.57 71.67 71.71 71.74 71.75 71.76 71.76 71.76 71.76 71.76 71.76

Likewise, in Fig. 5, shows the ciphertext expansion between proposed RNS-based N-prime model and N-prime model where the proposed model is able to reduce the growth of ciphertext file size by approximately 72% in comparison to N-prime model as shown in Table 3. This was achieved due to encryption process that was done based on residue number system with respect to moduli set. Finally, the security of the proposed model still depends on problem of factorization of integers to their primary numbers as mentioned in [7], such integers are product of multiple N-prime numbers and big prime number that serve as secret key that is unknown to attacker.

5 Conclusion In this paper, we proposed RNS-based N-prime model as an improvement over N-prime model for real-world applications of symmetric-based FHE scheme that requires lower latency and reduced ciphertext file size. The proposed model is based on residue number system approach and ensures parallel computation at encryption stage which brings about efficiency and robustness in addition to the homomorphic properties. The proposed model also deals explicitly with text after converting it to ASCII value instead of binary system, pass it to RNS forward conversion module in conjunction with moduli set and then pass it to the encryption block. The results show that the proposed model outperform N-prime model in terms of encryption execution time and ciphertext expansion at 72% reduction in ciphertext file size. Further research can be investigated to gain better decryption execution time, as it is taking larger execution time to decrypting ciphertext file back to the original plaintext or result of the computation than encrypting its corresponding plaintext.

Appendix A: Ciphertext [[0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24,

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5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [24, 5, 13], [23, 4, 12], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13],

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[23, 4, 12], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [24, 5, 13], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [24, 5, 13], [23, 4, 12], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [24, 5, 13], [24, 5, 13], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [24, 5, 13], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [24, 5, 13], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [24, 5, 13], [23, 4, 12], [0, 6, 14], [0, 6, 14], [24, 5, 13], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6, 14], [23, 4, 12], [23, 4, 12], [24, 5, 13], [0, 6, 14], [23, 4, 12], [0, 6, 14], [24, 5, 13], [0, 6, 14], [0, 6, 14], [23, 4, 12], [23, 4, 12], [0, 6, 14], [23, 4, 12], [0, 6,

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Big Data Analytics: Partitioned B+-Tree-Based Indexing in MapReduce Ali Usman Abdullahi , Rohiza Ahmad, and Nordin M. Zakaria

Abstract Big data analytics platforms are designed to improve performance by avoiding the extract transfer load approach. Also, there are techniques which have worked very well in performance optimization for relational databases. Yet these techniques are in the process of integration into big data analytics. Indexing and its data structure is an example of such techniques. Despite its popularity in query optimization for efficient data mining, the indexing was not integrated into the MapReduce platform. By design the MapReduce was made to perform a full scan of the input data. However, there were attempts made to incorporate the indexing for performance improvement in MapReduce in recent years. However, these attempts have not exhausted the potentials of indexing in the MapReduce query processing. Consequently, this chapter presents an indexing approach that uses the partitioned B+-Tree as its data structure to index the InputSplit component of the Hadoop distributed file system. This was done to achieve efficient data mining query processing when used with the Hadoop MapReduce. The results of this study showed that the proposed index method has significantly reduced the index size as well as the execution runtime of all search queries by at least 50% for all the used data sizes when compared with the Normal MapReduce processing and another clustered index approach. Thus, the use of the proposed index approach has the potential to significantly reduce the time taken in mining data within a dataset by half.

A. U. Abdullahi (B) Computer Science Education Department, Federal College of Education (Tech), Gombe, Nigeria e-mail: [email protected] URL: http://www.fcetgombe.edu.ng R. Ahmad · N. M. Zakaria Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia e-mail: [email protected] N. M. Zakaria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 H. Chiroma et al. (eds.), Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, https://doi.org/10.1007/978-3-030-66288-2_9

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1 Introduction Indexing, as an information retrieval technique, is the process of generating all the suitable data structures that allow for efficient retrieval of stored information [9, 21]. The term index referred to the suitable data structure needed to allow for the efficient information retrieval [21]. The data structures used in index in most cases do not store the information itself rather it uses other data structures and pointers to locate where the data has been stored. Also, the indexing have played a very important role in performance improvement of relational databases. The indexing techniques in relational database management systems (RDBMS) are well developed and matured [9, 18]. However, in big data analytics the indexing concept is still at the developmental stage and needs more attention from researchers as pointed out by Yang and Parker [34], An [2], Richter et al. [25] and Sevugan et al. [28]. There are two indexing concepts when it comes to indexing in big data analytics. The first one is the document indexing, which is the use of the MapReduce process to index the contents of any stored document for easy retrieval. Usually, the most used indexing technique for this task is the inverted index [21]. In this concept the main goal of the MapReduce job is the indexing of content of the document itself [20]. Early works of indexing in MapReduce were concentrated on this aspect. This was supported by the fact that the developers of MapReduce (Google) dealt mainly with searching through large document datasets [6, 7, 31]. Basically the search engine companies are interested in fast retrieval of their queried stored data [15, 18, 19, 30, 35]. The second concept is indexing for speeding up the process of the MapReduce’s job execution. The indexing here performs the same function it does with the traditional databases of restricting the query processing to only the input data to be affected by any given query [2, 5, 10, 17, 34]. This indexing is similar to the one found in most structured query language (SQL)-on-Hadoop, and even in other variants of distributed files systems (DFS), like BigTable. However, the way and manner in which the index approach works in the traditional database, SQL-on-Hadoop and even on table-based DFS, is that it is usually based on the storage structure, i.e., table for those platforms that use tables as storage structure [14, 16, 19]. This fact means that indexes are usually provided for each table making the amount of data to be scanned before query processing to increase. On the contrary, the user-defined indexing approach is quite different when it is used directly on Hadoop, where Hadoop distributed files systems (HDFS) components that handle the stored data are indexed [22, 33]. Particularly in the case of MapReduce, this indexing concept ensures the scanning of only those blocks/chunks/splits that are targeted by the query to be processed. The study presented in this chapter is focused on this second indexing concept as a way of improving big data analytics using the MapReduce programming paradigm for data mining [14]. This concept of indexing, if integrated into the MapReduce execution process can help solve the following problems: (1) The scanning of the whole input data, (2)

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The same ways of handling both low and high selectivity tasks and (3) The lack of robust access method for information retrieval in online access processing (OLAP) situations. In an effort to overcome these problems some researchers have attempted to incorporate the indexing mechanism into the MapReduce framework. For instance, Yang and Parker [34] have employed HDFS files as B-Tree nodes to achieve indexing. Also, An [2] used blockIds from the HDFS as B+Tree search keys for determining the start and the end of the contiguous blocks to scan, which is used as index. In addition, Richter et al. [25] used the copies of the replica stored by HDFS, to form a clustered index of different data attributes that may likely be used as incoming query predicates. Clustered indexes are built on those fields, which is subsequently used for query processing. In all of these studies, the authors used an indexing data structure that scale logarithmically, thereby preventing the MapReduce from performing a full scan of the input data. Thus, guiding the process is to just scan and process the data that corresponds to the output of the indexes. Notwithstanding the prevention of full scanning of the input data by these approaches, their indexes are built using all of the keys from the stored data. So, the full scanning of the indexing structure itself is done due to the nature of the components of the HDFS used. Furthermore, when the BTree and B+-Tree as indexing data structures are used in MapReduce it can facilitate the scanning of relevant portions of the stored data to answer range queries covering small sections (low selectivity) or wider sections of the data (high selectivity). However, the search algorithm in the B-Tree takes a non-uniform time for different types of queries, due to the storing of the search key’s corresponding values/records at all levels of the tree [29], while the search algorithm in the B+-Tree involves searching the whole of the tree all the time [3]. This is relevant because the search in the tree structures is the basic process that all other processes are based on. The search algorithm of B-Tree has been improved in the B+-Tree variants and those variants can be used to further improve the performance in the MapReduce query processing. Lastly, any indexing approach to be used in MapReduce should improve the two problems mentioned for the effectiveness of MapReduce in mining data stored in the warehouse for OLAP. Thus, the indexing approach proposed in the chapter chooses an improved variant of the B+-Tree called partitioned B+Tree introduced by Abdullahi et al. [1]. The partitioned B+-Tree provides an improved search algorithm, which ensures reduced execution time for query retrieval. This fact is what influences a quick record retrieval during mining of big data using MapReduce platforms [16, 26].

1.1 Objective of the Chapter The main objective of this chapter was to present an indexing approach that puts together partitioned B+-Tree as data structure and its algorithms for inserting and

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searching of data. The strategy of the proposed index approach is to restrict the amount of the input data to be processed by the MapReduce platform to only the part of the input data that is required for the query processing. The remaining sections of the chapter are as follows: Sect. 2 presents the review of the literatures, Sect. 3 discusses the methodology used in the study and Sect. 4 presents the experimental setup and the results of the experiments conducted. The conclusion and future work are discussed in Sect. 6.

2 Literature Review In this section the review of the two concepts of indexing that were used in MapReduce is presented.

2.1 Inverted Index in MapReduce The first concept of the indexing involves a simple inverted index which was said to be implemented trivially in MapReduce as one of the effective tasks for textual data retrieval. This was highlighted in the original paper on MapReduce [8] cited by Graefe and Kuno [12] and discussed in Cao et al. [4], Stewart et al. [32] and Mofidpoor et al. [22]. The inverted index works by parsing the split of the input data to the map function, which performs the filtering of the data as defined in it and emits a sequence of pairs. Then, the reduce function accepts all of the pairs of the same key, sorts the corresponding values and emits pair. The set of all the output pairs form a simple inverted index. McCreadie, Macdonald and Ounis [20] concluded that two interpretations of the above scenario can be a per-token indexing or a per-term indexing in relation to the indexing of corpus datasets: The per-token indexing strategy involves emitting pairs for each token in a document by the map function, while the reduce function does the aggregation of each unique term with its corresponding doc-ID to obtain the term frequencies (tf), after which the completed posting list for that term is written to a disk. On the other hand, the per-term indexing is the one in which the map function emits tuples in the form and this reduces the number of emitted operations as only unique terms per document are emitted. The reduce function only sorts the instances by document to obtain the final posting list sorted by ascending doc-ID. Apart from these two methods the inverted index is also used by MapReduce to index a whole document in what is called per-document indexing. In the per-document indexing, the map function emits tuples in the form of , while the reduce phase writes all the index structures. Though this strategy emits fewer key/value pairs, the value of each pair emitted used to have

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more data and has a reduced intermediate result. Thus, this approach achieves higher levels of compression than the single-term approaches. Also, the documents are easily indexed on the same reduce task due to the sorting of the document names [20]. In addition, there is the per-posting list indexing. This approach is based on a single-pass indexing, which splits data onto multiple map tasks with each operating on its own data subset. The map task serves as the scanning phase of the single-pass indexing. As this process runs on the document, a compressed posting list is built in the memory for each term. This partial index is flushed by the map task when the memory runs low or when all the documents are processed. The flushing is done by emitting a set in the form of pairs for all the term present in the memory. In all of these four strategies of document indexing in MapReduce the main task focus is on and carried out by the MapReduce job, which is the indexing of the document’s contents [20].

2.2 User-Defined Indexing in MapReduce The indexing here focuses on restricting the query processing to include only those parts of the input data to be effected by the query [2, 34]. For the MapReduce framework, this concept achieves its purpose by only scanning of the blocks or splits or files that contain the input data. This study has focused on the second indexing concept as a way of improving big data analytics using the MapReduce programming paradigm. In big data analytics, the second indexing concept has been initially seen as being less important or a costly one due to the fact that infrequent updating is not a common characteristic of big data. However, later works in the area indicated that indexing can play a major role in the information retrieval aspect of the big data analytics. This makes its benefit to surpass its cost. Yang and Parker [34], for instance, implemented indexing by employing a HDFS file as B-Tree node. The index used by the authors is considered non-clustered index [27]. This in turn was due to the fact that the reading of the index is not done sequentially. Their approach adds a primitive function to traverse the tree in order to locate the required segment of the data to be processed by their improved Map-reduce-merge-traverse version of MapReduce. This introduction of the B-Tree index has improved the performance of the MapReduce (changed to Map-reduce-merge-traverse). The improvement is up to 50% and above for a threenode cluster when compared to any number of nodes less than 10 with the normal MapReduce. On the other hand, An [2] used blockId as the B+-Tree’s search keys and the offset of the keys as a value to build an index. The built index is stored in a HDFS file as contiguous blocks rising from the node at the bottom to the root at the top. The index-based execution of the MapReduce is done by first searching for the keys that are contained in the queries’ predicate from the already built index. This is done to determine the starting and the end of the contiguous blocks that represent the search keys in the index. The blockIds that fall between the two points are those

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that are scanned for query processing by the MapReduce. With these two mentioned techniques, the amount of data to be scanned by the MapReduce is restricted. The B+Tree with blockIds as the keys index scan for MapReduce, outperformed the normal MapReduce by at most 50%, and below for varying percentage of I/O volume used by in the study. Furthermore, Richter et al. [25] used the copy of the replicas stored by the HDFS to index different data attributes that may likely be used as predicates of incoming queries. Their approach used a clustered index [24] based on each of these replicas. The clustered index is first scanned to get the blockId of the data to be processed from the index. Then, it uses the returned blockIds to locate the input data to be scanned and used by the MapReduce for processing the query in the question. Then, it used its customized input-split policy that helped their approach to determine the part of the input data that could take part in the query processing. After the index result is returned, the results of using their indexing with other Hadoop settings indicated a 20% reduction of the execution runtime.

3 Methodology This section of the chapter discusses the methodology used in the study. It gives the details of how various component of the study come together to achieve its object. Figure 1 shows the overall picture of the proposed indexing scheme.

Fig. 1 The block diagram of the scheme indexing for the MapReduce query processing

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The proposed approach basically consists of several components. They include the query, which is the statement that specifies how the process should be done; the rebuild index component, which is used for building and subsequent re-building of the index when needed. Then, there is the search index component, which is used for searching the index using the values provided in the query’s predicate. This component searches and returns the location of the data required by the query. Lastly, the scheme also has another component which is the query processing component. This component does the actual processing of the query been issued. The approach works first by building an index using partitioned B+-Tree (the data structure) with search keys from the input data and their corresponding values (inputsplits) using the rebuild index component. The built index is stored in a temporary file on the HDFS. When a query for certain processing is to be executed, its predicates will first be used to search the index to get the input-splits using the search index component. The search index component also uses the Pat B+-Tree to re-construct the index and use it to search and return the targeted results. By doing that, only the input-splits that cover the part of the input data that is required by the query are returned and stored in yet another temporary file on the HDFS. The returned input-splits are then read and used by the query processing component to process the given query. The framework view of the approach’s working flow, as mentioned, is captured in Fig. 1.

3.1 Partitioned B+-Tree The partitioned B+-Tree mentioned is an adaptation of the partitioned B-Tree [11], in which the author proposed the addition of an artificial lead key to the BTree. This addition resulted in a logical partitioning of the B-Tree based on run generations that feed the B-Tree during the initial index creation. Graefe [11] implemented his tree based on the traditional B-Tree. However, in this study the same idea was implemented with some improvements on B+Tree. The choice of partitioned B+-Tree for this study was due to its performance in a preliminary experiment in comparison with other variants of B+-Tree as reported in [1].

3.2 InputSplit as Component of Choice in the HDFS The corresponding values for the search keys used by the proposed indexing approach are the input-split components of the Hadoop distributed file system (HDFS). The input-split is the HDFS’s feature that uniquely identifies the chunks of data that the Hadoop made use of for the proper managing of the blocks of the stored data on its HDFS [13, 23, 36]. The reason for choosing the input-split is because it provides a better balance between the index size and searching cost on one side and the size of the input data

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to be scanned on the other. So, even if the blockId is to be used for indexing, the input-splits that cover those blocks have to be generated first, before extracting the blockIds from them. Index Rebuild in MapReduce First, the index needs to be built over the complete input data before it is used as mentioned earlier. A complete MapReduce program is used to extract the HDFS components alongside the search key values from the input data in order to build the index. Figure 2 has shown the expanded version of the rebuild index block of Fig. 1; here, the internal workings of the component are depicted. The rebuild index reads the inputs from the stored data on the HDFS using its Mapper class called the RebuildIndexMapper. This Mapper class has three methods, namely, setup(), map() and the cleanup(). The setup() method takes a context variable of the context class type as the argument. The context class is an optimized record reader/writer handler used by the Hadoop. The setup() method used the variable to extract the Hadoop internal component’s settings and properties. One such component is the input-split, which is the targeted component for the proposed index approach as mentioned earlier. Also, the context variable in the setup() method is used alongside the extracted input-split to further get the properties of other components, such as the filename of the input files. In some cases, the extracted filenames are concatenated with the extracted values from the records in order to form unique search key values. The use of the filename as part of the unique search key values is to avoid the treating of the keys from files with the same values as one and the same entry.

Fig. 2 Block diagram for index building process

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However, if the input data is contained in a larger file, then only the unique key values will be extracted and used as the index search keys. Then, the map() method of the RebuildIndexMapper class reads each record from the input file(s) using its defined key/value arguments. The method passes the values as arguments to an external method. The function of this external method is just to extract and return the values designated for use as the index search keys. After that, the input data is grouped in small files with similar values in the field intended for use in the index. Then, the map() method concatenates the returned values with the file names extracted in the setup() method to form the unique search key values; else, the returned values from the extra method are used as the search keys. The search key extracted formed together with the input-split for each record are passed to the Pat B+-Tree for index building. Furthermore, the index building is done by the build() method of the Pat B+-Tree. The build() builds the tree as the Map() method iterates over the input records and passes the key/value pairs to it. A modification was made to the insertion condition in the insertRecord() of the Pat B+-Tree to only allow the insertion of one search key value for a range of keys having the same input-split value. This was done to prevent multiple insertion of input-split values for all of the keys that fall under it, hence, compressing and compacting the index to a smaller size. In addition, the cleanup() method was used to capture the final state of the tree and to write the subtree as a single object from a chunk of the stored data. The cleanup() method calls the toString() method of the Pat B+-Tree, which in turn, prints the subtree onto temporary file(s) within the HDFS. The output at the mapper stage produces subtrees from the chunks of data processed by different map jobs. Searching the Index using MapReduce The second step for the integration, which was represented by the second block in Fig. 1, is the searching of the index. Figure 3 presents the details of the internal workings for the processes involved in the searching of the index. Here also, the mapper class’s only program is used to read from the index file generated by the previous scheme’s component, the rebuild index. This simply means that the program only has a mapper called the SearchIndexMapper class. Also, this mapper class has the same three methods as the previous one, which are: the setup(), the map() and cleanup() methods. The setup() uses its context variable as an argument to read the range of keys, to be searched for within the index, through the context variable’s configuration instance, which helps in accepting arguments at runtime. Then, the map() method of the SearchIndexMapper class reads each record from the index file(s) using its defined key/value arguments to rebuild the tree in the same way as described earlier. This is done to ensure that the tree is rebuilt and retained/cached by the Hadoop for faster searching. Algorithm 1 shows the pseudocode of the map() method highlighting the functions explained

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Fig. 3 Block diagram for the index searching process

Algorithm 1: Algorithm for SearchIndexMapper Class Input: Range of Keys Result: Input-Split values SearchIndexMapper extends Mapper{ Setup(Context context){ get configuration args at runtime } map (inkey,invalue, contetx){ if indexValue then split the indexValue if indexValuelength >= 3 then build Pat B+-Tree end end } cleanup (Context context){ iterate through the values returned by Pat B+-Tree search print with context(null, value) } }

After that, the cleanup() method of this class captures the final state of the tree and calls the searchRange() method of the Pat B+-Tree by passing the range of keys from the query’s predicates to it. The searchRange() method of the Pat B+-Tree has been modified to search and return the appropriate value that corresponds to any given search key. The search is done by comparing the given search key with a range of separator keys in the tree. This is possible due to the logic used in building and compressing the Pat B+-Tree explained earlier.

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Then, the cleanup() method uses its context’s write() method to iterate and print the returned values onto the output file(s) within the HDFS as the index’s result. Using the Index in MapReduce Execution Process: In order to make use of the index in the main MapReduce query execution process, i.e., query processing of Fig. 1, another complete MapReduce program is required. The program operates in a normal way as an ordinary MapReduce function, with the exception of using a customized FileInputformat. Figure 4 shows the processes, especially the IndexFileInputFormat component. The query processing uses three classes, namely, The Mapper, the Reducer and the IndexFileInputFormat classes. The Mapper class consists of two methods: the setup() and the map() methods. The setup() method of the Mapper class uses its context variables’ configuration property to get runtime arguments in the same way as explained in the details earlier. The map() method, on the other hand, iterates through the input data records comparing the keys/values from the records with the lower and upper bounds from the query’s predicates. The map() method emits only those records that have met the condition set by the query’s predicates. Then, the class’s getSplit() method calls yet another feeder method the readInputSplitFromFile() method that reads the index result which was returned by the SearchIndex class discussed earlier. The readInputSplitFromFile() method iterates

Fig. 4 Block diagram for integrating the index using IndexFileInputFormat class

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through the index of the result entries, which has “null’ as the value of all the keys and a string form of the input-splits as their corresponding values. The index results are the ones printed out during the searching of the index using the query’s predicates. The string form of the input-splits is not recognized by the FileInputFormat as valid input-splits, hence another method that transforms the strings into a valid input-split is needed.

4 Experimental Results and Discussion In this section the setup of the experiment as well as the results obtained and their interpretations are discussed.

4.1 The Dataset In this study, three different datasets were used. The dataset, which is related to oil and gas domain. Meteorological and Oceanography data (Modata) is the data used by the Oil and Gas (O&G) industries for exploration sites analysis and forecasting. Modata is quite different from the previous datasets discussed, because it comes in multiple small files, as opposed to one large file like the other benchmark datasets. Each file contains data for a single point out of several thousand others in an exploration site. In addition, the files have varied sizes according to the number of information/columns/parameters that they capture. For instance, files that have the minimum number of columns, i.e., 20 columns, have the size of 60 MB, while files which have maximum number of columns, i.e., 300 columns, have the size of 307 MB. Furthermore, each tuple in the file represents data taken for a particular hour of the day. For example, in a site called Sea Fine central (SF-Central), there are 14,881 points, and each point will have several tuples of data which are recorded every hour. Since site SF-Central is nearer to the shoreline, there are 20 parameters or features recorded in each tuple such as ‘wind direction’, ‘Wind speed’, ‘date’ and ‘time’. Besides raw data, other derived statistical data such as the ‘mean’ and the ‘variation’ of the parameters are also included in the data collection. For sites that are more toward the ocean, the parameters collected will be even more. Moreover, each file contains the tuples described for six decades. From this description of the Modata, one can imagine the hugeness and complexity the data files. Figure 5 shows sample of data from one file of Modata. On the other hand, for the Modata, a record volume of 10, 20, 40, 80, 160 and 320 million were queried from the data sizes mentioned above, respectively. Also, Table 1 shows the summary of the query sets with their expected returning records.

Big Data Analytics: Partitioned B+-Tree-Based Indexing …

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Fig. 5 Sample of data from Modata files

Table 1 Data sizes and expected returning records for Modata

S/no.

Data size

Number of target records

1

20 GB

10,000,000

2

50 GB

20,000,000

3

100 GB

40,000,000

4

200 GB

80,000,000

5

500 GB

160,000,000

6

1 TB

320,000,000

4.2 Index Building Using the Datasets On the other hand, the search keys for the Modata were formed by concatenating the individual filename to the timestamps. The timestamps are the field with the unique values in each of the files. However, the same timestamps are found in all other files, which are in their thousands in the Modata dataset. Hence, the search keys in the Modata are lengthier. For the search keys’ corresponding values, the proposed index method used the inputsplit component of the HDFS. The decision to use the input-split comes through the observation made from the reviewed literatures. This is due to the fact that, using any other HDFS components required the authors to develop algorithms to work with the index.

4.3 Test Queries The queries used in the experiment of this research were formed into sets targeting the restrictive clauses that were relevant to indexing, namely, WHERE, JOIN and

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Table 2 Sample select queries with WHERE, JOIN and GROUP BY for Modata Query type no

MOData dataset

1

select * from Modata.Mdata20 where gpt =‘1956-07-31 00:00:00’ AND ‘SF000320’ and ‘2000-12-31 00:00:00’ distribute by wd

2

select sc.gpt, sc.wd, sc.datadate, mk.gpt, mk.wd, Modata mk.datadate from Modata.Mdata10a sf JOIN Modata.Mdata10b sc ON sc.datadate = sf.datadate where sc.gpt